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Ziri D, Hugueville L, Olivier C, Boulinguez P, Gunasekaran H, Lau B, Welter ML, George N. Inhibitory control of gait initiation in humans: An electroencephalography study. Psychophysiology 2024; 61:e14647. [PMID: 38987662 DOI: 10.1111/psyp.14647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/18/2024] [Accepted: 06/26/2024] [Indexed: 07/12/2024]
Abstract
Response inhibition is a crucial component of executive control. Although mainly studied in upper limb tasks, it is fully implicated in gait initiation. Here, we assessed the influence of proactive and reactive inhibitory control during gait initiation in healthy adult participants. For this purpose, we measured kinematics and electroencephalography (EEG) activity (event-related potential [ERP] and time-frequency data) during a modified Go/NoGo gait initiation task in 23 healthy adults. The task comprised Go-certain, Go-uncertain, and NoGo conditions. Each trial included preparatory and imperative stimuli. Our results showed that go-uncertainty resulted in delayed reaction time, without any difference for the other parameters of gait initiation. Proactive inhibition, that is, Go uncertain versus Go certain conditions, influenced EEG activity as soon as the preparatory stimulus. Moreover, both proactive and reactive inhibition influenced the amplitude of the ERPs (central P1, occipito-parietal N1, and N2/P3) and theta and alpha/low beta band activities in response to the imperative-Go-uncertain versus Go-certain and NoGo versus Go-uncertain-stimuli. These findings demonstrate that the uncertainty context; induced proactive inhibition, as reflected in delayed gait initiation. Proactive and reactive inhibition elicited extended and overlapping modulations of ERP and time-frequency activities. This study shows the protracted influence of inhibitory control in gait initiation.
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Affiliation(s)
- Deborah Ziri
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Laurent Hugueville
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, CENIR, Paris, France
| | - Claire Olivier
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- PANAM Core Facility, CENIR, Paris Brain Institute, Paris, France
| | - Philippe Boulinguez
- INSERM, CNRS, Lyon Neuroscience Research Center, Université de Lyon, Lyon, France
| | - Harish Gunasekaran
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Brian Lau
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Marie-Laure Welter
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, CENIR, Paris, France
- Department of Neurophysiology, Rouen University Hospital and University of Rouen, Rouen, France
| | - Nathalie George
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, Inserm, CNRS, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, CENIR, Paris, France
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Hazarika D, Vishnu KN, Ransing R, Gupta CN. Dynamical Embedding of Single-Channel Electroencephalogram for Artifact Subspace Reconstruction. SENSORS (BASEL, SWITZERLAND) 2024; 24:6734. [PMID: 39460214 PMCID: PMC11510769 DOI: 10.3390/s24206734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Revised: 10/07/2024] [Accepted: 10/12/2024] [Indexed: 10/28/2024]
Abstract
This study introduces a novel framework to apply the artifact subspace reconstruction (ASR) algorithm on single-channel electroencephalogram (EEG) data. ASR is known for its ability to remove artifacts like eye-blinks and movement but traditionally relies on multiple channels. Embedded ASR (E-ASR) addresses this by incorporating a dynamical embedding approach. In this method, an embedded matrix is created from single-channel EEG data using delay vectors, followed by ASR application and reconstruction of the cleaned signal. Data from four subjects with eyes open were collected using Fp1 and Fp2 electrodes via the CameraEEG android app. The E-ASR algorithm was evaluated using metrics like relative root mean square error (RRMSE), correlation coefficient (CC), and average power ratio. The number of eye-blinks with and without the E-ASR approach was also estimated. E-ASR achieved an RRMSE of 43.87% and had a CC of 0.91 on semi-simulated data and effectively reduced artifacts in real EEG data, with eye-blink counts validated against ground truth video data. This framework shows potential for smartphone-based EEG applications in natural environments with minimal electrodes.
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Affiliation(s)
- Doli Hazarika
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati 781039, India; (D.H.); (K.N.V.)
| | - K. N. Vishnu
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati 781039, India; (D.H.); (K.N.V.)
| | - Ramdas Ransing
- Department of Psychiatry, Clinical Neurosciences, and Addiction Medicine, All India Institute of Medical Sciences, Guwahati 781101, India;
| | - Cota Navin Gupta
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati 781039, India; (D.H.); (K.N.V.)
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Lee W, Kim E, Park J, Eo J, Jeong B, Park HJ. Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis. Neuroimage 2024; 299:120797. [PMID: 39159703 DOI: 10.1016/j.neuroimage.2024.120797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/24/2024] [Accepted: 08/14/2024] [Indexed: 08/21/2024] Open
Abstract
Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.
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Affiliation(s)
- Wooyong Lee
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Euisun Kim
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Jiyoung Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea
| | - Jinseok Eo
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea
| | - Bumseok Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute for Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hae-Jeong Park
- Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
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Giangrande A, Botter A, Piitulainen H, Cerone GL. Motion Artifacts in Dynamic EEG Recordings: Experimental Observations, Electrical Modelling, and Design Considerations. SENSORS (BASEL, SWITZERLAND) 2024; 24:6363. [PMID: 39409399 PMCID: PMC11479364 DOI: 10.3390/s24196363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 09/19/2024] [Accepted: 09/27/2024] [Indexed: 10/20/2024]
Abstract
Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.
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Affiliation(s)
- Alessandra Giangrande
- Laboratory of Neuromuscular System and Rehabilitation Engineering, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (A.G.); (A.B.)
- Faculty of Sport and Health Sciences, University of Jyväskylä, 40014 Jyväskylä, Finland;
| | - Alberto Botter
- Laboratory of Neuromuscular System and Rehabilitation Engineering, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (A.G.); (A.B.)
| | - Harri Piitulainen
- Faculty of Sport and Health Sciences, University of Jyväskylä, 40014 Jyväskylä, Finland;
| | - Giacinto Luigi Cerone
- Laboratory of Neuromuscular System and Rehabilitation Engineering, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (A.G.); (A.B.)
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Mathewson KE, Kuziek JP, Scanlon JEM, Robles D. The moving wave: Applications of the mobile EEG approach to study human attention. Psychophysiology 2024; 61:e14603. [PMID: 38798056 DOI: 10.1111/psyp.14603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024]
Abstract
Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.
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Affiliation(s)
- Kyle E Mathewson
- Department of Psychology, Faculty of Science, University of Alberta, Edmonton, Alberta, Canada
| | - Jonathan P Kuziek
- Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | - Daniel Robles
- Department of Psychology, Rutgers University, Piscataway, New Jersey, USA
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Barnes L, Davidson MJ, Alais D. The speed and phase of locomotion dictate saccade probability and simultaneous low-frequency power spectra. Atten Percept Psychophys 2024:10.3758/s13414-024-02932-4. [PMID: 39048846 DOI: 10.3758/s13414-024-02932-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2024] [Indexed: 07/27/2024]
Abstract
Every day we make thousands of saccades and take thousands of steps as we explore our environment. Despite their common co-occurrence in a typical active state, we know little about the coordination between eye movements, walking behaviour and related changes in cortical activity. Technical limitations have been a major impediment, which we overcome here by leveraging the advantages of an immersive wireless virtual reality (VR) environment with three-dimensional (3D) position tracking, together with simultaneous recording of eye movements and mobile electroencephalography (EEG). Using this approach with participants engaged in unencumbered walking along a clear, level path, we find that the likelihood of eye movements at both slow and natural walking speeds entrains to the rhythm of footfall, peaking after the heel-strike of each step. Compared to previous research, this entrainment was captured in a task that did not require visually guided stepping - suggesting a persistent interaction between locomotor and visuomotor functions. Simultaneous EEG recordings reveal a concomitant modulation entrained to heel-strike, with increases and decreases in oscillatory power for a broad range of frequencies. The peak of these effects occurred in the theta and alpha range for slow and natural walking speeds, respectively. Together, our data show that the phase of the step-cycle influences other behaviours such as eye movements, and produces related modulations of simultaneous EEG following the same rhythmic pattern. These results reveal gait as an important factor to be considered when interpreting saccadic and time-frequency EEG data in active observers, and demonstrate that saccadic entrainment to gait may persist throughout everyday activities.
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Affiliation(s)
- Lydia Barnes
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | | | - David Alais
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
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Richer N, Bradford JC, Ferris DP. Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research. Neurosci Biobehav Rev 2024; 162:105718. [PMID: 38744350 DOI: 10.1016/j.neubiorev.2024.105718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/18/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.
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Affiliation(s)
- Natalie Richer
- Department of Kinesiology and Applied Health, University of Winnipeg, Winnipeg, Manitoba, Canada.
| | - J Cortney Bradford
- US Army Combat Capabilities Development Command US Army Research Laboratory, Adelphi, MD, USA
| | - Daniel P Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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Callan DE, Torre–Tresols JJ, Laguerta J, Ishii S. Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA. FRONTIERS IN NEUROERGONOMICS 2024; 5:1358660. [PMID: 38989056 PMCID: PMC11233536 DOI: 10.3389/fnrgo.2024.1358660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024]
Abstract
Introduction To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. Methods A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. Discussion This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.
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Affiliation(s)
- Daniel E. Callan
- Brain Information Communication Research Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Institut Supérieur de l'Aéronautique et de l'Espace, University of Toulouse, Toulouse, France
| | - Juan Jesus Torre–Tresols
- Brain Information Communication Research Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Institut Supérieur de l'Aéronautique et de l'Espace, University of Toulouse, Toulouse, France
| | - Jamie Laguerta
- Brain Information Communication Research Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Department of Integrated Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Shin Ishii
- Brain Information Communication Research Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Graduate School of Informatics, Kyoto University, Kyoto, Japan
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De Sanctis P, Mahoney JR, Wagner J, Blumen HM, Mowrey W, Ayers E, Schneider C, Orellana N, Molholm S, Verghese J. Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study. JMIR Res Protoc 2024; 13:e56726. [PMID: 38842914 PMCID: PMC11190628 DOI: 10.2196/56726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia syndromes. However, not much is known about the neural signature of functional decline, particularly during the very early stages of dementia. Early intervention before overt impairment is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias. However, to justify early intervention, those at risk need to be detected earlier and more accurately. The decline in complex daily function (CdF) such as managing medications has been reported to precede impairment in basic activities of daily living (eg, eating and dressing). OBJECTIVE Our goal is to establish the neural signature of decline in CdF during the preclinical dementia period. METHODS Gait is central to many CdF and community-based activities. Hence, to elucidate the neural signature of CdF, we validated a novel electroencephalographic approach to measuring gait-related brain activation while participants perform complex gait-based functional tasks. We hypothesize that dementia-related pathology during the preclinical period activates a unique gait-related electroencephalographic (grEEG) pattern that predicts a subsequent decline in CdF. RESULTS We provide preliminary findings showing that older adults reporting CdF limitations can be characterized by a unique gait-related neural signature: weaker sensorimotor and stronger motor control activation. This subsample also had smaller brain volume and white matter hyperintensities in regions affected early by dementia and engaged in less physical exercise. We propose a prospective observational cohort study in cognitively unimpaired older adults with and without subclinical AD (plasma amyloid-β) and vascular (white matter hyperintensities) pathologies. We aim to (1) establish the unique grEEG activation as the neural signature and predictor of decline in CdF during the preclinical dementia period; (2) determine associations between dementia-related pathologies and incidence of the neural signature of CdF; and (3) establish associations between a dementia risk factor, physical inactivity, and the neural signature of CdF. CONCLUSIONS By establishing the clinical relevance and biological basis of the neural signature of CdF decline, we aim to improve prediction during the preclinical stages of ADs and other dementias. Our approach has important research and translational implications because grEEG protocols are relatively inexpensive and portable, and predicting CdF decline may have real-world benefits. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/56726.
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Affiliation(s)
- Pierfilippo De Sanctis
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Pediatrics, Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jeannette R Mahoney
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Johanna Wagner
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Helena M Blumen
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wenzhu Mowrey
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Emmeline Ayers
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Claudia Schneider
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Natasha Orellana
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Sophie Molholm
- Department of Pediatrics, Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Joe Verghese
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, United States
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Song BG, Kang N. Removal of movement artifacts and assessment of mental stress analyzing electroencephalogram of non-driving passengers under whole-body vibration. Front Neurosci 2024; 18:1328704. [PMID: 38726034 PMCID: PMC11079143 DOI: 10.3389/fnins.2024.1328704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
The discomfort caused by whole-body vibration (WBV) has long been assessed using subjective surveys or objective measurements of body acceleration. However, surveys have the disadvantage that some of participants often express their feelings in a capricious manner, and acceleration data cannot take into account individual preferences and experiences of their emotions. In this study, we investigated vibration-induced mental stress using the electroencephalogram (EEG) of 22 seated occupants excited by random vibrations. Between the acceleration and the EEG signal, which contains electrical noise due to the head shaking caused by random vibrations, we found that there was a strong correlation, which acts as an artifact in the EEG, and therefore we removed it using an adaptive filter. After removing the artifact, we analyzed the characteristics of the brainwaves using topographic maps and observed that the activities detected in the frontal electrodes showed significant differences between the static and vibration conditions. Further, frontal alpha asymmetry (FAA) and relative band power indices in the frontal electrodes were analyzed statistically to assess mental stress under WBV. As the vibration level increased, EEG analysis in the frontal electrodes showed a decrease in FAA and alpha power but an increase in gamma power. These results are in good agreement with the literature in the sense that FAA and alpha band power decreases with increasing stress, thus demonstrating that WBV causes mental stress and that the stress increases with the vibration level. EEG assessment of stress during WBV is expected to be used in the evaluation of ride comfort alongside existing self-report and acceleration methods.
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Affiliation(s)
- Byoung-Gyu Song
- Department of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Namcheol Kang
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
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11
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Weisberg SM, Ebner NC, Seidler RD. Getting LOST: A conceptual framework for supporting and enhancing spatial navigation in aging. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2024; 15:e1669. [PMID: 37933623 PMCID: PMC10939954 DOI: 10.1002/wcs.1669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023]
Abstract
Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids-devices and depictions-which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition Neuroscience > Behavior.
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Affiliation(s)
- Steven M. Weisberg
- Department of Psychology, University of Florida, 945 Center Dr., Gainesville, FL 32611
- Center for Cognitive Aging and Memory, Department of Clinical and Health Psychology, University of Florida, 1225 Center Dr., Gainesville, FL 32611
| | - Natalie C. Ebner
- Department of Psychology, University of Florida, 945 Center Dr., Gainesville, FL 32611
- Center for Cognitive Aging and Memory, Department of Clinical and Health Psychology, University of Florida, 1225 Center Dr., Gainesville, FL 32611
- Institute on Aging, University of Florida, 2004 Mowry Rd., Gainesville, FL 32611
- Department of Physiology and Aging, University of Florida, 1345 Center Drive, Gainesville, FL 32610-0274
| | - Rachael D. Seidler
- Department of Applied Physiology & Kinesiology, University of Florida, 1864 Stadium Rd., Gainesville, FL 32611
- Department of Neurology, University of Florida, 1149 Newell Dr., Gainesville, FL 32611
- Normal Fixel Institute for Neurological Diseases, University of Florida, 3009 SW Williston Rd. 1864 Stadium Rd., Gainesville, FL 32608
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12
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Tharawadeepimuk K, Limroongreungrat W, Pilanthananond M, Nanbancha A. Auditory Cue Effects on Gait-Phase-Dependent Electroencephalogram (EEG) Modulations during Overground and Treadmill Walking. SENSORS (BASEL, SWITZERLAND) 2024; 24:1548. [PMID: 38475084 DOI: 10.3390/s24051548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5-4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement.
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Affiliation(s)
| | | | | | - Ampika Nanbancha
- College of Sports Science and Technology, Mahidol University, Nakhon Pathom 73170, Thailand
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13
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Khajuria A, Sharma R, Joshi D. EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation. Clin EEG Neurosci 2024; 55:143-163. [PMID: 36052404 DOI: 10.1177/15500594221123690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.
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Affiliation(s)
- Aayushi Khajuria
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Richa Sharma
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Deepak Joshi
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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14
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Mustile M, Kourtis D, Edwards MG, Ladouce S, Volpe D, Pilleri M, Pelosin E, Learmonth G, Donaldson DI, Ietswaart M. Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles. Brain Commun 2023; 5:fcad326. [PMID: 38107501 PMCID: PMC10724048 DOI: 10.1093/braincomms/fcad326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 10/03/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023] Open
Abstract
The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.
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Affiliation(s)
- Magda Mustile
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
- The Psychological Sciences Research Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
| | - Dimitrios Kourtis
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
| | - Martin G Edwards
- The Psychological Sciences Research Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
| | - Simon Ladouce
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Daniele Volpe
- Fresco Parkinson Center, Villa Margherita, S. Stefano Riabilitazione, 36100 Vicenza, Italy
| | - Manuela Pilleri
- Fresco Parkinson Center, Villa Margherita, S. Stefano Riabilitazione, 36100 Vicenza, Italy
| | - Elisa Pelosin
- Ospedale Policlinico San Martino, IRCCS, 16132 Genova, Italy
| | - Gemma Learmonth
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
- School of Psychology & Neuroscience, University of Glasgow, Glasgow, G12 8QQ, UK
| | - David I Donaldson
- School of Psychology and Neuroscience, University of St Andrews, St. Andrews, KY16 9AJ, UK
| | - Magdalena Ietswaart
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
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15
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Schmoigl-Tonis M, Schranz C, Müller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci 2023; 17:1251690. [PMID: 37920561 PMCID: PMC10619676 DOI: 10.3389/fnhum.2023.1251690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.
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Affiliation(s)
- Mathias Schmoigl-Tonis
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Christoph Schranz
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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16
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Yang SY, Lin YP. Movement Artifact Suppression in Wearable Low-Density and Dry EEG Recordings Using Active Electrodes and Artifact Subspace Reconstruction. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3844-3853. [PMID: 37751338 DOI: 10.1109/tnsre.2023.3319355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Wearable low-density dry electroencephalogram (EEG) headsets facilitate multidisciplinary applications of brain-activity decoding and brain-triggered interaction for healthy people in real-world scenarios. However, movement artifacts pose a great challenge to their validity in users with naturalistic behaviors (i.e., without highly controlled settings in a laboratory). High-precision, high-density EEG instruments commonly embed an active electrode infrastructure and/or incorporate an auxiliary artifact subspace reconstruction (ASR) pipeline to handle movement artifact interferences. Existing endeavors motivate this study to explore the efficacy of both hardware and software solutions in low-density and dry EEG recordings against non-tethered settings, which are rarely found in the literature. Therefore, this study employed a LEGO-like electrode-holder assembly grid to coordinate three 3-channel system designs (with passive/active dry vs. passive wet electrodes). It also conducted a simultaneous EEG recording while performing an oddball task during treadmill walking, with speeds of 1 and 2 KPH. The quantitative metrics of pre-stimulus noise, signal-to-noise ratio, and inter-subject correlation from the collected event-related potentials of 18 subjects were assessed. Results indicate that while treating a passive-wet system as benchmark, only the active-electrode design more or less rectified movement artifacts for dry electrodes, whereas the ASR pipeline was substantially compromised by limited electrodes. These findings suggest that a lightweight, minimally obtrusive dry EEG headset should at least equip an active-electrode infrastructure to withstand realistic movement artifacts for potentially sustaining its validity and applicability in real-world scenarios.
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17
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Ebers MR, Rosenberg MC, Kutz JN, Steele KM. A machine learning approach to quantify individual gait responses to ankle exoskeletons. J Biomech 2023; 157:111695. [PMID: 37406604 DOI: 10.1016/j.jbiomech.2023.111695] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023]
Abstract
Predicting an individual's response to an exoskeleton and understanding what data are needed to characterize responses remains challenging. Specifically, we lack a theoretical framework capable of quantifying heterogeneous responses to exoskeleton interventions. We leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals' exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928-0.963) and electromyography (0.665-0.788), (p<0.042)) than the Nominal model (median R2 for kinematics (0.863-0.939) and electromyography (0.516-0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954-0.977) and electromyography (0.724-0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.
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Affiliation(s)
- Megan R Ebers
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
| | - Michael C Rosenberg
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
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18
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Monroe DC, Berry NT, Fino PC, Rhea CK. A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations. SENSORS (BASEL, SWITZERLAND) 2023; 23:6296. [PMID: 37514591 PMCID: PMC10385586 DOI: 10.3390/s23146296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.
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Affiliation(s)
- Derek C Monroe
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
| | - Nathaniel T Berry
- Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, NC 27402, USA
- Under Armour, Inc., Innovation, Baltimore, MD 21230, USA
| | - Peter C Fino
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher K Rhea
- College of Health Sciences, Old Dominion University, Norfolk, VA 23508, USA
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19
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Swerdloff MM, Hargrove LJ. Dry EEG measurement of P3 to evaluate cognitive load during sitting, standing, and walking. PLoS One 2023; 18:e0287885. [PMID: 37410768 PMCID: PMC10325065 DOI: 10.1371/journal.pone.0287885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 06/14/2023] [Indexed: 07/08/2023] Open
Abstract
Combining brain imaging with dual-task paradigms provides a quantitative, direct metric of cognitive load that is agnostic to the motor task. This work aimed to quantitatively assess cognitive load during activities of daily living-sitting, standing, and walking-using a commercial dry encephalography headset. We recorded participants' brain activity while engaging in a stimulus paradigm that elicited event-related potentials. The stimulus paradigm consisted of an auditory oddball task in which participants had to report the number of oddball tones that were heard during each motor task. We extracted the P3 event-related potential, which is inversely proportional to cognitive load, from EEG signals in each condition. Our main findings showed that P3 was significantly lower during walking compared to sitting (p = .039), suggesting that cognitive load was higher during walking compared to the other activities. There were no significant differences in P3 between sitting and standing. Head motion did not have a significant impact on the measurement of cognitive load. This work validates the use of a commercial dry-EEG headset for measuring cognitive load across different motor tasks. The ability to accurately measure cognitive load in dynamic activities opens new avenues for exploring cognitive-motor interactions in individuals with and without motor impairments. This work highlights the potential of dry EEG for measuring cognitive load in naturalistic settings.
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Affiliation(s)
- Margaret M. Swerdloff
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois, United States of America
| | - Levi J. Hargrove
- Department of Biomedical Engineering, Northwestern University, Chicago, Illinois, United States of America
- Regenstein Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, Illinois, United States of America
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20
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Mazzacane S, Coccagna M, Manzella F, Pagliarini G, Sironi VA, Gatti A, Caselli E, Sciavicco G. Towards an objective theory of subjective liking: A first step in understanding the sense of beauty. PLoS One 2023; 18:e0287513. [PMID: 37352316 PMCID: PMC10289447 DOI: 10.1371/journal.pone.0287513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/07/2023] [Indexed: 06/25/2023] Open
Abstract
The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting. Starting from a proprietary dataset of 248 trials from 16 subjects exposed to art paintings, using a real ecological context, this paper analyses the application of a novel symbolic machine learning technique, specifically designed to extract information from unstructured data and to express it in form of logical rules. Our purpose is to extract qualitative and quantitative logical rules, to relate the voltage at specific frequencies and in specific electrodes, and that, within the limits of the experiment, may help to understand the brain process that drives liking or disliking experiences in human subjects.
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Affiliation(s)
- S. Mazzacane
- CIAS Interdepartmental Research Center (Dept. of Architecture, Dept. of Chemical, Pharmaceutical and Agricultural Sciences), University of Ferrara, Ferrara, Italy
| | - M. Coccagna
- CIAS Interdepartmental Research Center (Dept. of Architecture, Dept. of Chemical, Pharmaceutical and Agricultural Sciences), University of Ferrara, Ferrara, Italy
| | - F. Manzella
- Dept. of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
| | - G. Pagliarini
- Dept. of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
| | - V. A. Sironi
- CESPEB Research Center, Neuroaesthetic Laboratory, University Bicocca, Milan, Italy
| | - A. Gatti
- Dept. of Humanistic Studies, University of Ferrara, Ferrara, Italy
| | - E. Caselli
- CIAS Interdepartmental Research Center (Dept. of Architecture, Dept. of Chemical, Pharmaceutical and Agricultural Sciences), University of Ferrara, Ferrara, Italy
| | - G. Sciavicco
- Dept. of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
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21
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Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the 'real world'. Nat Rev Neurosci 2023; 24:347-362. [PMID: 37046077 PMCID: PMC10642288 DOI: 10.1038/s41583-023-00692-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/14/2023]
Abstract
Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.
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Affiliation(s)
- Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Sabrina L Maoz
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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22
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Wireless EEG: A survey of systems and studies. Neuroimage 2023; 269:119774. [PMID: 36566924 DOI: 10.1016/j.neuroimage.2022.119774] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/18/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.
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23
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Taheri Gorji H, Wilson N, VanBree J, Hoffmann B, Petros T, Tavakolian K. Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight. Sci Rep 2023; 13:2507. [PMID: 36782004 PMCID: PMC9925430 DOI: 10.1038/s41598-023-29647-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/08/2023] [Indexed: 02/15/2023] Open
Abstract
Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.
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Affiliation(s)
- Hamed Taheri Gorji
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND, USA.
| | - Nicholas Wilson
- Departments of Aviation, University of North Dakota, Grand Forks, ND, USA
| | - Jessica VanBree
- Department of Psychology, University of North Dakota, Grand Forks, ND, USA
| | - Bradley Hoffmann
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND, USA
| | - Thomas Petros
- Department of Psychology, University of North Dakota, Grand Forks, ND, USA
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND, USA
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De Sanctis P, Wagner J, Molholm S, Foxe JJ, Blumen HM, Horsthuis DJ. Neural signature of mobility-related everyday function in older adults at-risk of cognitive impairment. Neurobiol Aging 2023; 122:1-11. [PMID: 36463848 PMCID: PMC10281759 DOI: 10.1016/j.neurobiolaging.2022.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 10/27/2022] [Accepted: 11/03/2022] [Indexed: 11/10/2022]
Abstract
Assessment of everyday activities is central to the diagnosis of dementia. Yet, little is known about brain processes associated with everyday functional limitations, particularly during early stages of cognitive decline. Twenty-six older adults (mean = 74.9 y) were stratified by risk using the Montreal Cognitive Assessment battery (MoCA, range: 0- 30) to classify individuals as higher (22-26) and lower risk (27+) of cognitive impairment. We investigated everyday function using a gait task designed to destabilize posture and applied Mobile Brain/Body Imaging. We predicted that participants would increase step width to gain stability, yet the underlying neural signatures would be different for lower versus higher risk individuals. Step width and fronto-parietal activation increased during visually perturbed input. Frontomedial theta increased in higher risk individuals during perturbed and unperturbed inputs. Left sensorimotor beta decreased in lower risk individuals during visually perturbed input. Modulations in theta and beta power were associated with MoCA scores. Our findings suggest that older adults at-risk of cognitive impairment can be characterized by a unique neural signature of everyday function.
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Affiliation(s)
- Pierfilippo De Sanctis
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Johanna Wagner
- Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, USA
| | - Sophie Molholm
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; The Dominick P. Purpura Department of Neuroscience, Rose F. Kennedy Intellectual and Developmental Disabilities Research Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John J Foxe
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; The Cognitive Neurophysiology Laboratory, The Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Helena M Blumen
- Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, USA
| | - Douwe J Horsthuis
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
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Domingos C, Marôco JL, Miranda M, Silva C, Melo X, Borrego C. Repeatability of Brain Activity as Measured by a 32-Channel EEG System during Resistance Exercise in Healthy Young Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1992. [PMID: 36767358 PMCID: PMC9914944 DOI: 10.3390/ijerph20031992] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/17/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
Electroencephalography (EEG) is attracting increasing attention in the sports and exercise fields, as it provides insights into brain behavior during specific tasks. However, it remains unclear if the promising wireless EEG caps provide reliable results despite the artifacts associated with head movement. The present study aims to evaluate the repeatability of brain activity as measured by a wireless 32-channel EEG system (EMOTIV flex cap) during resistance exercises in 18 apparently healthy but physically inactive young adults (10 men and 8 women). Moderate-intensity leg press exercises are performed with two evaluations with 48 h. between. This intensity allows enough time for data analysis while reducing unnecessary but involuntary head movements. Repeated measurements of EEG during the resistance exercise show high repeatability in all frequency bands, with excellent ICCs (>0.90) and bias close to zero, regardless of sex. These results suggest that a 32-channel wireless EEG system can be used to collect data on controlled resistance exercise tasks performed at moderate intensities. Future studies should replicate these results with a bigger sample size and different resistance exercises and intensities.
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Affiliation(s)
- Christophe Domingos
- CIEQV, Escola Superior de Desporto de Rio Maior, Instituto Politécnico de Santarém, Av. Dr. Mário Soares nº 110, 2040-413 Rio Maior, Portugal
| | - João Luís Marôco
- Exercise and Health Sciences Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Marco Miranda
- Department of Physics, Instituto Superior Técnico, University of Lisbon, 1749-016 Lisbon, Portugal
- Department of Bioengineering, LaSEEB-Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
| | - Carlos Silva
- CIEQV, Escola Superior de Desporto de Rio Maior, Instituto Politécnico de Santarém, Av. Dr. Mário Soares nº 110, 2040-413 Rio Maior, Portugal
| | - Xavier Melo
- Centro Interdisciplinar de Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, 1496-751 Oeiras, Portugal
- Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Egas Moniz School of Health & Science, Caparica, 2829-511 Almada, Portugal
| | - Carla Borrego
- CIEQV, Escola Superior de Desporto de Rio Maior, Instituto Politécnico de Santarém, Av. Dr. Mário Soares nº 110, 2040-413 Rio Maior, Portugal
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Gonsisko CB, Ferris DP, Downey RJ. iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:928. [PMID: 36679726 PMCID: PMC9863946 DOI: 10.3390/s23020928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.
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Korivand S, Jalili N, Gong J. Experiment protocols for brain-body imaging of locomotion: A systematic review. Front Neurosci 2023; 17:1051500. [PMID: 36937690 PMCID: PMC10014824 DOI: 10.3389/fnins.2023.1051500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
Introduction Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings. Methods This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols. Results The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments. Discussion Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.
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Affiliation(s)
- Soroush Korivand
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
| | - Nader Jalili
- Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL, United States
| | - Jiaqi Gong
- Department of Computer Science, The University of Alabama, Tuscaloosa, AL, United States
- *Correspondence: Jiaqi Gong
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Ocklenburg S, Peterburs J. Monitoring Brain Activity in VR: EEG and Neuroimaging. Curr Top Behav Neurosci 2023; 65:47-71. [PMID: 37306852 DOI: 10.1007/7854_2023_423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Virtual reality (VR) is increasingly used in neuroscientific research to increase ecological validity without sacrificing experimental control, to provide a richer visual and multisensory experience, and to foster immersion and presence in study participants, which leads to increased motivation and affective experience. But the use of VR, particularly when coupled with neuroimaging or neurostimulation techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), or transcranial magnetic stimulation (TMS), also yields some challenges. These include intricacies of the technical setup, increased noise in the data due to movement, and a lack of standard protocols for data collection and analysis. This chapter examines current approaches to recording, pre-processing, and analyzing electrophysiological (stationary and mobile EEG), as well as neuroimaging data recorded during VR engagement. It also discusses approaches to synchronizing these data with other data streams. In general, previous research has used a range of different approaches to technical setup and data processing, and detailed reporting of procedures is urgently needed in future studies to ensure comparability and replicability. More support for open-source VR software as well as the development of consensus and best practice papers on issues such as the handling of movement artifacts in mobile EEG-VR will be essential steps in ensuring the continued success of this exciting and powerful technique in neuroscientific research.
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Affiliation(s)
- Sebastian Ocklenburg
- Department of Psychology, Faculty for Life Sciences, MSH Medical School Hamburg, Hamburg, Germany.
- ICAN Institute for Cognitive and Affective Neuroscience, MSH Medical School Hamburg, Hamburg, Germany.
- Faculty of Psychology, Institute of Cognitive Neuroscience, Biopsychology, Ruhr University Bochum, Bochum, Germany.
| | - Jutta Peterburs
- Institute of Systems Medicine & Department of Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
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Ertl M, Zu Eulenburg P, Woller M, Mayadali Ü, Boegle R, Dieterich M. Vestibular mapping of the naturalistic head-centered motion spectrum. J Vestib Res 2023; 33:299-312. [PMID: 37458057 DOI: 10.3233/ves-210121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND Naturalistic head accelerations can be used to elicit vestibular evoked potentials (VestEPs). These potentials allow for analysis of cortical vestibular processing and its multi-sensory integration with a high temporal resolution. METHODS We report the results of two experiments in which we compared the differential VestEPs elicited by randomized translations, rotations, and tilts in healthy subjects on a motion platform. RESULTS An event-related potential (ERP) analysis revealed that established VestEPs were verifiable in all three acceleration domains (translations, rotations, tilts). A further analysis of the VestEPs showed a significant correlation between rotation axes (yaw, pitch, roll) and the amplitude of the evoked potentials. We found increased amplitudes for rotations in the roll compared to the pitch and yaw plane. A distributed source localization analysis showed that the activity in the cingulate sulcus visual (CSv) area best explained direction-dependent amplitude modulations of the VestEPs, but that the same cortical network (posterior insular cortex, CSv) is involved in processing vestibular information, regardless of the motion direction. CONCLUSION The results provide evidence for an anisotropic, direction-dependent processing of vestibular input by cortical structures. The data also suggest that area CSv plays an integral role in ego-motion perception and interpretation of spatial features such as acceleration direction and intensity.
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Affiliation(s)
- Matthias Ertl
- Department of Psychology, University of Bern, Bern, Switzerland
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Peter Zu Eulenburg
- German Center for Vertigo and Balance Disorders (IFBLMU), Ludwig-Maximilians-Universität München, Munich, Germany
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marie Woller
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ümit Mayadali
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Rainer Boegle
- German Center for Vertigo and Balance Disorders (IFBLMU), Ludwig-Maximilians-Universität München, Munich, Germany
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marianne Dieterich
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Vertigo and Balance Disorders (IFBLMU), Ludwig-Maximilians-Universität München, Munich, Germany
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany
- Munich Cluster for Neurology (SyNergy), Munich, Germany
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Palucci Vieira LH, Carling C, da Silva JP, Santinelli FB, Polastri PF, Santiago PRP, Barbieri FA. Modelling the relationships between EEG signals, movement kinematics and outcome in soccer kicking. Cogn Neurodyn 2022; 16:1303-1321. [PMID: 36408067 PMCID: PMC9666621 DOI: 10.1007/s11571-022-09786-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 01/03/2022] [Accepted: 01/21/2022] [Indexed: 12/16/2022] Open
Abstract
The contribution of cortical activity (e.g. EEG recordings) in various brain regions to motor control during goal-directed manipulative tasks using lower limbs remains unexplored. Therefore, the aim of the current study was to determine the magnitude of associations between EEG-derived brain activity and soccer kicking parameters. Twenty-four under-17 players performed an instep kicking task (18 m from the goal) aiming to hit 1 × 1 m targets allocated in the goalpost upper corners in the presence of a goalkeeper. Using a portable 64-channel EEG system, brain oscillations in delta, theta, alpha, beta and gamma frequency bands were determined at the frontal, motor, parietal and occipital regions separately for three phases of the kicks: preparatory, approach and immediately prior to ball contact. Movement kinematic measures included segmental linear and relative velocities, angular joint displacement and velocities. Mean radial error and ball velocity were assumed as outcome indicators. A significant influence of frontal theta power immediately prior to ball contact was observed in the variance of ball velocity (R 2 = 35%, P = 0.01) while the expression of occipital alpha component recorded during the preparatory phase contributed to the mean radial error (R 2 = 20%, P = 0.049). Ankle eversion angle at impact moment likely mediated the association between frontal theta power and subsequent ball velocity (β = 0.151, P = 0.06). The present analysis showed that the brain signalling at cortical level may be determinant in movement control, ball velocity and accuracy when performing kick attempts from the edge of penalty area. Trial registration number #RBR-8prx2m-Brazilian Registry of Clinical Trials ReBec. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09786-2.
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Affiliation(s)
- Luiz H. Palucci Vieira
- Human Movement Research Laboratory (MOVI-LAB), Faculty of Sciences, Graduate Program in Movement Sciences, Department of Physical Education, São Paulo State University (Unesp), Av. Eng. Luís Edmundo Carrijo Coube, 2085 - Nucleo Res. Pres. Geisel, Bauru, SP 17033-360 Brazil
| | | | - João Pedro da Silva
- Human Movement Research Laboratory (MOVI-LAB), Faculty of Sciences, Graduate Program in Movement Sciences, Department of Physical Education, São Paulo State University (Unesp), Av. Eng. Luís Edmundo Carrijo Coube, 2085 - Nucleo Res. Pres. Geisel, Bauru, SP 17033-360 Brazil
| | - Felipe B. Santinelli
- Human Movement Research Laboratory (MOVI-LAB), Faculty of Sciences, Graduate Program in Movement Sciences, Department of Physical Education, São Paulo State University (Unesp), Av. Eng. Luís Edmundo Carrijo Coube, 2085 - Nucleo Res. Pres. Geisel, Bauru, SP 17033-360 Brazil
| | - Paula F. Polastri
- Laboratory of Information, Vision and Action (LIVIA), São Paulo State University (Unesp), Faculty of Sciences, Department of Physical Education, Graduate Program in Movement Sciences, Bauru, Brazil
| | - Paulo R. P. Santiago
- Biomechanics and Motor Control Laboratory (LaBioCoM), School of Physical Education and Sport of Ribeirão Preto (EEFERP), University of São Paulo (USP), Ribeirão Preto, Brazil
| | - Fabio A. Barbieri
- Human Movement Research Laboratory (MOVI-LAB), Faculty of Sciences, Graduate Program in Movement Sciences, Department of Physical Education, São Paulo State University (Unesp), Av. Eng. Luís Edmundo Carrijo Coube, 2085 - Nucleo Res. Pres. Geisel, Bauru, SP 17033-360 Brazil
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Simonetti D, Koopman B, Sartori M. Automated estimation of ankle muscle EMG envelopes and resulting plantar-dorsi flexion torque from 64 garment-embedded electrodes uniformly distributed around the human leg. J Electromyogr Kinesiol 2022; 67:102701. [PMID: 36096035 DOI: 10.1016/j.jelekin.2022.102701] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 12/14/2022] Open
Abstract
The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.
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Affiliation(s)
- Donatella Simonetti
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands.
| | - Bart Koopman
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands
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Jacobsen NSJ, Blum S, Scanlon JEM, Witt K, Debener S. Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait. Front Sports Act Living 2022; 4:945341. [PMID: 36275441 PMCID: PMC9582531 DOI: 10.3389/fspor.2022.945341] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/13/2022] [Indexed: 11/09/2022] Open
Abstract
Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.
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Affiliation(s)
- Nadine Svenja Josée Jacobsen
- Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany,*Correspondence: Nadine Svenja Josée Jacobsen
| | - Sarah Blum
- Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany,Hörzentrum Oldenburg GmbH, Oldenburg, Germany,Cluster of Excellence Hearing4all, Oldenburg, Germany
| | - Joanna Elizabeth Mary Scanlon
- Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany,Branch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany
| | - Karsten Witt
- Department of Neurology and Research Center Neurosensory Science, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany,Cluster of Excellence Hearing4all, Oldenburg, Germany
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Wang G, Yang Y, Wang J, Hao Z, Luo X, Liu J. Dynamic changes of brain networks during standing balance control under visual conflict. Front Neurosci 2022; 16:1003996. [PMID: 36278015 PMCID: PMC9581155 DOI: 10.3389/fnins.2022.1003996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Stance balance control requires a very accurate tuning and combination of visual, vestibular, and proprioceptive inputs, and conflict among these sensory systems may induce posture instability and even falls. Although there are many human mechanics and psychophysical studies for this phenomenon, the effects of sensory conflict on brain networks and its underlying neural mechanisms are still unclear. Here, we combined a rotating platform and a virtual reality (VR) headset to control the participants’ physical and visual motion states, presenting them with incongruous (sensory conflict) or congruous (normal control) physical-visual stimuli. Further, to investigate the effects of sensory conflict on stance stability and brain networks, we recorded and calculated the effective connectivity of source-level electroencephalogram (EEG) and the average velocity of the plantar center of pressure (COP) in healthy subjects (18 subjects: 10 males, 8 females). First, our results showed that sensory conflict did have a detrimental effect on stance posture control [sensor F(1, 17) = 13.34, P = 0.0019], but this effect decreases over time [window*sensor F(2, 34) = 6.72, P = 0.0035]. Humans show a marked adaptation to sensory conflict. In addition, we found that human adaptation to the sensory conflict was associated with changes in the cortical network. At the stimulus onset, congruent and incongruent stimuli had similar effects on brain networks. In both cases, there was a significant increase in information interaction centered on the frontal cortices (p < 0.05). Then, after a time window, synchronized with the restoration of stance stability under conflict, the connectivity of large brain regions, including posterior parietal, visual, somatosensory, and motor cortices, was generally lower in sensory conflict than in controls (p < 0.05). But the influence of the superior temporal lobe on other cortices was significantly increased. Overall, we speculate that a posterior parietal-centered cortical network may play a key role in integrating congruous sensory information. Furthermore, the dissociation of this network may reflect a flexible multisensory interaction strategy that is critical for human posture balance control in complex and changing environments. In addition, the superior temporal lobe may play a key role in processing conflicting sensory information.
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Affiliation(s)
- Guozheng Wang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi Yang
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, China
| | - Jian Wang
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, China
| | - Zengming Hao
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xin Luo
- Department of Sports Science, College of Education, Zhejiang University, Hangzhou, China
| | - Jun Liu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- *Correspondence: Jun Liu,
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Chuang CH, Chang KY, Huang CS, Jung TP. IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal. Neuroimage 2022; 263:119586. [PMID: 36031182 DOI: 10.1016/j.neuroimage.2022.119586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 10/31/2022] Open
Abstract
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://github.com/roseDwayane/AIEEG.
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Affiliation(s)
- Chun-Hsiang Chuang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Department of Education and Learning Technology, National Tsing Hua University, Hsinchu, Taiwan.
| | - Kong-Yi Chang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
| | - Chih-Sheng Huang
- Department of Artificial Intelligence Research and Development, Elan Microelectronics Corporation, Hsinchu, Taiwan; College of Artificial Intelligence and Green Energy, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, Taiwan
| | - Tzyy-Ping Jung
- Institute of Engineering in Medicine and Institute for Neural Computation, University of California San Diego, La Jolla, USA
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35
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Studnicki A, Downey RJ, Ferris DP. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155867. [PMID: 35957423 PMCID: PMC9371038 DOI: 10.3390/s22155867] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 05/27/2023]
Abstract
Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.
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36
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 DOI: 10.1109/access.2022.3159155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/27/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh
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37
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Hossain MS, Chowdhury MEH, Reaz MBI, Ali SHM, Bakar AAA, Kiranyaz S, Khandakar A, Alhatou M, Habib R, Hossain MM. Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22093169. [PMID: 35590859 PMCID: PMC9102309 DOI: 10.3390/s22093169] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/24/2022] [Accepted: 03/25/2022] [Indexed: 05/14/2023]
Abstract
The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
- Correspondence: (M.E.H.C.); (M.B.I.R.)
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (M.S.H.); (S.H.M.A.); (A.A.A.B.)
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (S.K.); (A.K.)
| | - Mohammed Alhatou
- Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar;
| | - Rumana Habib
- Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh;
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38
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Spaces Eliciting Negative and Positive Emotions in Shrinking Neighbourhoods: a Study in Seoul, South Korea, Using EEG (Electroencephalography). J Urban Health 2022; 99:245-259. [PMID: 35312914 PMCID: PMC9033910 DOI: 10.1007/s11524-022-00608-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/07/2022] [Indexed: 10/18/2022]
Abstract
Although shrinking neighbourhoods are places where urban citizens experience negative emotions, some evidence suggests that people in some shrinking neighbourhoods feel less negative emotions than in other areas. Nevertheless, empirical studies that analyse environmental and personal elements that affect people's emotions in a shrinking neighbourhood remain insufficient. This is rather surprising, considering an increasing interest in the effects of negative emotions on individuals' health. Thus, this study used electroencephalography (EEG) to examine the impacts of environmental and personal characteristics on people's emotional levels in a shrinking area of Seoul, South Korea. A multilinear regression model was used to analyse emotional valence levels between sites with different urban designs and management levels. The results revealed that people felt positive emotions at sites where both urban design factors and their management were both satisfactory at appropriate levels. The results also found that people who had lived or worked in the neighbourhood for a long time and were women experienced more positive emotions than visitors and men. This finding implies that a shrinking neighbourhood can maintain a sense of satisfaction as long as the area is carefully managed. Revealing the emotional effects of environmental and personal characteristics in a shrinking neighbourhood can be used for planning practices and policy-making to create healthy and liveable urban neighbourhoods.
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39
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Putzolu M, Samogin J, Cosentino C, Mezzarobba S, Bonassi G, Lagravinese G, Vato A, Mantini D, Avanzino L, Pelosin E. Neural oscillations during motor imagery of complex gait: an HdEEG study. Sci Rep 2022; 12:4314. [PMID: 35279682 PMCID: PMC8918338 DOI: 10.1038/s41598-022-07511-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/20/2022] [Indexed: 11/15/2022] Open
Abstract
The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.
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40
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Gorjan D, Gramann K, De Pauw K, Marusic U. Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng 2022; 19. [PMID: 35147512 DOI: 10.1088/1741-2552/ac542c] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/08/2022] [Indexed: 11/12/2022]
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.
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Affiliation(s)
- Dasa Gorjan
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
| | - Klaus Gramann
- Technische Universität Berlin, Fasanenstr. 1, Berlin, Berlin, 10623, GERMANY
| | - Kevin De Pauw
- Vrije Universiteit Brussel, Pleinlaan 2, Brussel, Brussel, 1050, BELGIUM
| | - Uros Marusic
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
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41
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Flüthmann N, Kato K, Breuer J, Bloch O, Vogt T. Sports-Related Motor Processing at Different Rates of Force Development. J Mot Behav 2022; 54:588-598. [PMID: 35139750 DOI: 10.1080/00222895.2022.2033676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
How does our brain manage to process vast quantities of sensory information that define movement performance? By extracting the required movement parameters for which brain dynamics are, inter alia, assumed to be functionally related to, we used electroencephalography to investigate motor-related brain oscillations. Visually guided movement (i.e., motor) tasks at explosive, medium and slow rates of force development (RFD) revealed increased broad-band activity at explosive RFD, whereas decreasing activity could be observed during both intermediate and slow RFD. Moreover, a continuously decreasing activity pattern from faster to slower RFD and a return to baseline activity after full muscle relaxation was found. We suggest oscillatory activity to desynchronize in sensorimotor demanding tasks, whereas task-specific synchronization mirrors movement acceleration. The pre/post-stimulus activity steady state may indicate an inhibitory baseline that provides attentional focus and timing.
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Affiliation(s)
- Nils Flüthmann
- Institute of Professional Sport Education and Sport Qualifications, German Sport University Cologne, Cologne, Germany
| | - Kouki Kato
- Faculty of Sport Sciences, Waseda University, Tokorozawa City, Japan.,Physical Education Center, Nanzan University, Nagoya, Japan
| | - Jonas Breuer
- Institute of Professional Sport Education and Sport Qualifications, German Sport University Cologne, Cologne, Germany
| | - Oliver Bloch
- Biomechanics Performance Diagnostics Laboratory, Olympic Training Centre Rhineland, Cologne, Germany
| | - Tobias Vogt
- Institute of Professional Sport Education and Sport Qualifications, German Sport University Cologne, Cologne, Germany.,Faculty of Sport Sciences, Waseda University, Tokorozawa City, Japan
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42
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Chikhi S, Matton N, Blanchet S. EEG
power spectral measures of cognitive workload: A meta‐analysis. Psychophysiology 2022; 59:e14009. [DOI: 10.1111/psyp.14009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/13/2021] [Accepted: 01/10/2022] [Indexed: 12/22/2022]
Affiliation(s)
- Samy Chikhi
- Laboratoire Mémoire, Cerveau et Cognition (MC2Lab, URP 7536), Institute of Psychology University of Paris Boulogne‐Billancourt France
| | - Nadine Matton
- CLLE‐LTC University of Toulouse, CNRS (UMR5263) Toulouse France
- ENAC Research Lab École Nationale d’Aviation Civile Toulouse France
| | - Sophie Blanchet
- Laboratoire Mémoire, Cerveau et Cognition (MC2Lab, URP 7536), Institute of Psychology University of Paris Boulogne‐Billancourt France
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43
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Saul MA, He X, Black S, Charles F. A Two-Person Neuroscience Approach for Social Anxiety: A Paradigm With Interbrain Synchrony and Neurofeedback. Front Psychol 2022; 12:568921. [PMID: 35095625 PMCID: PMC8796854 DOI: 10.3389/fpsyg.2021.568921] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 12/06/2021] [Indexed: 12/11/2022] Open
Abstract
Social anxiety disorder has been widely recognised as one of the most commonly diagnosed mental disorders. Individuals with social anxiety disorder experience difficulties during social interactions that are essential in the regular functioning of daily routines; perpetually motivating research into the aetiology, maintenance and treatment methods. Traditionally, social and clinical neuroscience studies incorporated protocols testing one participant at a time. However, it has been recently suggested that such protocols are unable to directly assess social interaction performance, which can be revealed by testing multiple individuals simultaneously. The principle of two-person neuroscience highlights the interpersonal aspect of social interactions that observes behaviour and brain activity from both (or all) constituents of the interaction, rather than analysing on an individual level or an individual observation of a social situation. Therefore, two-person neuroscience could be a promising direction for assessment and intervention of the social anxiety disorder. In this paper, we propose a novel paradigm which integrates two-person neuroscience in a neurofeedback protocol. Neurofeedback and interbrain synchrony, a branch of two-person neuroscience, are discussed in their own capacities for their relationship with social anxiety disorder and relevance to the paradigm. The newly proposed paradigm sets out to assess the social interaction performance using interbrain synchrony between interacting individuals, and to employ a multi-user neurofeedback protocol for intervention of the social anxiety.
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Affiliation(s)
- Marcia A. Saul
- Faculty of Media and Communication, Centre for Digital Entertainment, Bournemouth University, Poole, United Kingdom
| | - Xun He
- Department of Psychology, Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom
- *Correspondence: Xun He
| | - Stuart Black
- Applied Neuroscience Solutions Ltd., Frimley Green, United Kingdom
| | - Fred Charles
- Department of Creative Technology, Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom
- Fred Charles
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44
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Using EEG to study sensorimotor adaptation. Neurosci Biobehav Rev 2022; 134:104520. [PMID: 35016897 DOI: 10.1016/j.neubiorev.2021.104520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/10/2021] [Accepted: 12/30/2021] [Indexed: 11/23/2022]
Abstract
Sensorimotor adaptation, or the capacity to flexibly adapt movements to changes in the body or the environment, is crucial to our ability to move efficiently in a dynamic world. The field of sensorimotor adaptation is replete with rigorous behavioural and computational methods, which support strong conceptual frameworks. An increasing number of studies have combined these methods with electroencephalography (EEG) to unveil insights into the neural mechanisms of adaptation. We review these studies: discussing EEG markers of adaptation in the frequency and the temporal domain, EEG predictors for successful adaptation and how EEG can be used to unmask latent processes resulting from adaptation, such as the modulation of spatial attention. With its high temporal resolution, EEG can be further exploited to deepen our understanding of sensorimotor adaptation.
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45
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Chang M, Büchel D, Reinecke K, Lehmann T, Baumeister J. Ecological validity in exercise neuroscience research: A systematic investigation. Eur J Neurosci 2022; 55:487-509. [PMID: 34997653 DOI: 10.1111/ejn.15595] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/27/2021] [Accepted: 01/03/2022] [Indexed: 11/28/2022]
Abstract
The contribution of cortical processes to adaptive motor behaviour is of great interest in the field of exercise neuroscience. Next to established criteria of objectivity, reliability and validity, ecological validity refers to the concerns of whether measurements and behaviour in research settings are representative of the real world. Because exercise neuroscience investigations using mobile electroencephalography are oftentimes conducted in laboratory settings under controlled environments, methodological approaches may interfere with the idea of ecological validity. This review utilizes an original ecological validity tool to assess the degree of ecological validity in current exercise neuroscience research. A systematic literature search was conducted to identify articles investigating cortical dynamics during goal-directed sports movement. To assess ecological validity, five elements (environment, stimulus, response, body and mind) were assessed on a continuum of artificiality-naturality and simplicity-complexity. Forty-seven studies were included in the present review. Results indicate lowest average ratings for the element of environment. The elements stimulus, body and mind had mediocre ratings, and the element of response had the highest overall ratings. In terms of the type of sport, studies that assessed closed-skill indoor sports had the highest ratings, whereas closed-skill outdoor sports had the lowest overall rating. Our findings identify specific elements that are lacking in ecological validity and areas of improvement in current exercise neuroscience literature. Future studies may potentially increase ecological validity by moving from reductionist, artificial environments towards complex, natural environments and incorporating real-world sport elements such as adaptive responses and competition.
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Affiliation(s)
- Melissa Chang
- Exercise Science & Neuroscience Unit, Department of Exercise & Health, Paderborn University, Paderborn, Germany
| | - Daniel Büchel
- Exercise Science & Neuroscience Unit, Department of Exercise & Health, Paderborn University, Paderborn, Germany
| | - Kirsten Reinecke
- Institute of Sports Medicine, Department of Exercise & Health, Paderborn University, Paderborn, Germany
| | - Tim Lehmann
- Exercise Science & Neuroscience Unit, Department of Exercise & Health, Paderborn University, Paderborn, Germany
| | - Jochen Baumeister
- Exercise Science & Neuroscience Unit, Department of Exercise & Health, Paderborn University, Paderborn, Germany
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46
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Robertson CV, Skein M, Wingfield G, Hunter JR, Miller TD, Hartmann TE. Acute electroencephalography responses during incremental exercise in those with mental illness. Front Psychiatry 2022; 13:1049700. [PMID: 36713924 PMCID: PMC9878313 DOI: 10.3389/fpsyt.2022.1049700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
INTRODUCTION Depression is a mental illness (MI) characterized by a process of behavioral withdrawal whereby people experience symptoms including sadness, anhedonia, demotivation, sleep and appetite change, and cognitive disturbances. Frontal alpha asymmetry (FAA) differs in depressive populations and may signify affective responses, with left FAA corresponding to such aversive or withdrawal type behavior. On an acute basis, exercise is known to positively alter affect and improve depressive symptoms and this has been measured in conjunction with left FAA as a post-exercise measure. It is not yet known if these affective electroencephalography (EEG) responses to exercise occur during exercise or only after completion of an exercise bout. This study therefore aimed to measure EEG responses during exercise in those with MI. MATERIALS AND METHODS Thirty one participants were allocated into one of two groups; those undergoing management of a mental health disorder (MI; N = 19); or reporting as apparently healthy (AH; N = 12). EEG responses at rest and during incremental exercise were measured at the prefrontal cortex (PFC) and the motor cortex (MC). EEG data at PFC left side (F3, F7, FP1), PFC right side (F4, F8, FP2), and MC (C3, Cz, and C4) were analyzed in line with oxygen uptake at rest, 50% of ventilatory threshold (VT) (50% VT) and at VT. RESULTS EEG responses increased with exercise across intensity from rest to 50% VT and to VT in all bandwidths (P < 0.05) for both groups. There were no significant differences in alpha activity responses between groups. Gamma responses in the PFC were significantly higher in MI on the left side compared to AH (P < 0.05). CONCLUSION Alpha activity responses were no different between groups at rest or any exercise intensity. Therefore the alpha activity response previously shown post-exercise was not found during exercise. However, increased PFC gamma activity in the MI group adds to the body of evidence showing increased gamma can differentiate between those with and without MI.
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Affiliation(s)
- C V Robertson
- School of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, NSW, Australia
| | - M Skein
- School of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, NSW, Australia
| | - G Wingfield
- Western NSW Local Health District, Dubbo, NSW, Australia
| | - J R Hunter
- School of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, NSW, Australia.,Holsworth Research Initiative, La Trobe University, Bendigo, VIC, Australia
| | - T D Miller
- School of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, NSW, Australia
| | - T E Hartmann
- School of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, NSW, Australia
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Ramanoël S, Durteste M, Delaux A, de Saint Aubert JB, Arleo A. Future trends in brain aging research: Visuo-cognitive functions at stake during mobility and spatial navigation. AGING BRAIN 2022; 2:100034. [PMID: 36908887 PMCID: PMC9997160 DOI: 10.1016/j.nbas.2022.100034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/28/2022] Open
Abstract
Aging leads to a complex pattern of structural and functional changes, gradually affecting sensorimotor, perceptual, and cognitive processes. These multiscale changes can hinder older adults' interaction with their environment, progressively reducing their autonomy in performing tasks relevant to everyday life. Autonomy loss can further be aggravated by the onset and progression of neurodegenerative disorders (e.g., age-related macular degeneration at the sensory input level; and Alzheimer's disease at the cognitive level). In this context, spatial cognition offers a representative case of high-level brain function that involves multimodal sensory processing, postural control, locomotion, spatial orientation, and wayfinding capabilities. Hence, studying spatial behavior and its neural bases can help identify early markers of pathogenic age-related processes. Until now, the neural correlates of spatial cognition have mostly been studied in static conditions thereby disregarding perceptual (other than visual) and motor aspects of natural navigation. In this review, we first demonstrate how visuo-motor integration and the allocation of cognitive resources during locomotion lie at the heart of real-world spatial navigation. Second, we present how technological advances such as immersive virtual reality and mobile neuroimaging solutions can enable researchers to explore the interplay between perception and action. Finally, we argue that the future of brain aging research in spatial navigation demands a widespread shift toward the use of naturalistic, ecologically valid experimental paradigms to address the challenges of mobility and autonomy decline across the lifespan.
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Affiliation(s)
- Stephen Ramanoël
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France.,Université Côte d'Azur, LAMHESS, Nice, France
| | - Marion Durteste
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | - Alexandre Delaux
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
| | | | - Angelo Arleo
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012 Paris, France
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Lee YE, Shin GH, Lee M, Lee SW. Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running. Sci Data 2021; 8:315. [PMID: 34930915 PMCID: PMC8688416 DOI: 10.1038/s41597-021-01094-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 11/08/2021] [Indexed: 11/24/2022] Open
Abstract
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.
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Affiliation(s)
- Young-Eun Lee
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Gi-Hwan Shin
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Minji Lee
- grid.222754.40000 0001 0840 2678Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841 Republic of Korea
| | - Seong-Whan Lee
- Korea University, Department of Brain and Cognitive Engineering, Seoul, 02841, Republic of Korea. .,Korea University, Department of Artificial Intelligence, Seoul, 02841, Republic of Korea.
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Wunderlich A, Vogel O, Šömen MM, Peskar M, Fricke M, Gramann K, Protzak J, Marusic U, Wollesen B. Dual-Task Performance in Hearing-Impaired Older Adults-Study Protocol for a Cross-Sectional Mobile Brain/Body Imaging Study. Front Aging Neurosci 2021; 13:773287. [PMID: 34867299 PMCID: PMC8633949 DOI: 10.3389/fnagi.2021.773287] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 10/19/2021] [Indexed: 11/25/2022] Open
Abstract
Background: Hearing impairments are associated with reduced walking performance under Dual-task (DT) conditions. Little is known about the neural representation of DT performance while walking in this target group compared to healthy controls or younger adults. Therefore, utilizing the Mobile Brain/Body Imaging approach (MoBI), we aim at gaining deeper insights into the brain dynamics underlying the interaction of cognitive and motor processes during different DT conditions (visual and auditory) controlling for age and the potential performance decrements of older adults with hearing impairments. Methods: The cross-sectional study integrates a multifactorial mixed-measure design. Between-subject factors grouping the sample will be age (younger vs. older adults) and hearing impairment (mild vs. not hearing impaired). The within-subject factors will be the task complexity (single- vs. DT) and cognitive task modality (visual vs. auditory). Stimuli of the cognitive task will vary according to the stimulus modality (visual vs. auditory), presentation side (left vs. right), and presentation-response compatibility (ipsilateral vs. contralateral). Analyses of DT costs and underlying neuronal correlates focus either on gait or cognitive performance. Based on an a priori sample size calculation 96 (48 healthy and 48 mildly hearing impaired) community-dwelling older adults (50–70 years) and 48 younger adults (20–30 years) will be recruited. Gait parameters of speed and rhythm will be captured. EEG activity will be recorded using 64 active electrodes. Discussion: The study evaluates cognitive-motor interference (CMI) in groups of young and older adults as well as older adults with hearing impairment. The underlying processes of the interaction between motor and cognitive tasks will be identified at a behavioral and neurophysiological level comparing an auditory or a visual secondary task. We assume that performance differences are linked to different cognitive-motor processes, i.e., stimulus input, resource allocation, and movement execution. Moreover, for the different DT conditions (auditory vs. visual) we assume performance decrements within the auditory condition, especially for older, hearing-impaired adults. Findings will provide evidence of general mechanisms of CMI (ST vs. DT walking) as well as task-specific effects in dual-task performance while over ground walking.
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Affiliation(s)
- Anna Wunderlich
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany
| | - Oliver Vogel
- Human Movement and Training Science, Institute of Human Movement Science, Psychology and Human Movement, University Hamburg, Hamburg, Germany
| | - Maja Maša Šömen
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
| | - Manca Peskar
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany.,Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia
| | - Madeleine Fricke
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany
| | - Klaus Gramann
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany
| | - Janna Protzak
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany
| | - Uros Marusic
- Science and Research Centre Koper, Institute for Kinesiology Research, Koper, Slovenia.,Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Bettina Wollesen
- Biological Psychology and Neuroergonomics, Department of Psychology and Ergonomics, Faculty V: Mechanical Engineering and Transport Systems, Technische Universität Berlin, Berlin, Germany.,Human Movement and Training Science, Institute of Human Movement Science, Psychology and Human Movement, University Hamburg, Hamburg, Germany
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50
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Xiao R, Huang Y, Xu R, Wang B, Wang X, Jin J. Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI. Cogn Neurodyn 2021; 16:791-803. [PMID: 35847541 PMCID: PMC9279536 DOI: 10.1007/s11571-021-09752-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 09/27/2021] [Accepted: 10/24/2021] [Indexed: 11/29/2022] Open
Abstract
In the motor-imagery (MI) based brain computer interface (BCI), multi-channel electroencephalogram (EEG) is often used to ensure the complete capture of physiological phenomena. With the redundant information and noise, EEG signals cannot be easily converted into separable features through feature extraction algorithms. Channel selection algorithms are proposed to address the issue, in which the filtering technique is widely used with the advantages of low computational cost and strong practicability. In this study, we proposed several improved methods for filtering channel selection algorithm. Specifically, based on the coefficient of variation and inter-class distance, a novel channel classification method was designed, which divided channels into different categories based on their contribution to feature extraction process. Then a filtering channel selection algorithm was proposed according to the previous classification method. Moreover, a new testing framework for filtering channel selection algorithms was proposed, which can better reflect the generalization ability of the algorithm. Experimental results indicated that the proposed channel classification method is effective, and the proposed testing framework is better than the original one. Meanwhile, the proposed channel selection algorithm achieved the accuracy of 87.7% and 81.7% in two BCI competition datasets, respectively, which was superior to competing algorithms.
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