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Piskin D, Müller R, Büchel D, Lehmann T, Baumeister J. Behavioral and cortical dynamics underlying superior accuracy in short-distance passes. Behav Brain Res 2024; 471:115120. [PMID: 38905733 DOI: 10.1016/j.bbr.2024.115120] [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: 04/06/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 06/23/2024]
Abstract
Improved pass accuracy is a prominent determinant of success in football. It demands an effective interaction of complex behavioral and cortical dynamics. Exploring differences in the ability to sustain an accurate pass behavior in a stable setting and the associated cortical dynamics at different expertise levels may provide an insight into skilled strategies contributing to superior accuracy in football. The aim of this study is to compare trial-to-trial variability of pass biomechanics and the corresponding cortical dynamics during short-distance passes between novices and experienced football players. Thirty participants (15 novices, 15 football players) performed 90 short-distance passes. The intertrial variability of pass biomechanics (foot acceleration, range of hip flexion, knee flexion and foot rotation) was assessed by means of multiscale entropy. The task-related cortical dynamics were analyzed via source-derived event-related spectral perturbations. Experienced players demonstrated higher accuracy and overall lower entropy values across multiple time scales which was significant for hip flexion. The electroencephalography data revealed group differences in parieto-occipital alpha desynchronization and frontal theta synchronization in successive phases of passes. The current findings suggest that experienced football players may show a skilled ability to recruit and retain pass biomechanics promoting higher accuracy, whereas novices may show an explorative behavior with higher spatial variability. This difference may be associated with distinctive visuospatial and attentional strategies acquired with expertise in football. Our study provides an insight into expertise-specific behavioral and cortical dynamics of superior accuracy in football and a basis for its prospective investigation in enriched contexts.
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Affiliation(s)
- Daghan Piskin
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany.
| | - Romina Müller
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Daniel Büchel
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Tim Lehmann
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
| | - Jochen Baumeister
- Department Sports & Health, Exercise Science & Neuroscience Unit, Paderborn University, Paderborn 33098, Germany
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2
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Kaveh R, Schwendeman C, Pu L, Arias AC, Muller R. Wireless ear EEG to monitor drowsiness. Nat Commun 2024; 15:6520. [PMID: 39095399 DOI: 10.1038/s41467-024-48682-7] [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: 10/01/2023] [Accepted: 05/09/2024] [Indexed: 08/04/2024] Open
Abstract
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
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Affiliation(s)
- Ryan Kaveh
- University of California Berkeley, Berkeley, CA, 94708, USA.
| | | | - Leslie Pu
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Ana C Arias
- University of California Berkeley, Berkeley, CA, 94708, USA
| | - Rikky Muller
- University of California Berkeley, Berkeley, CA, 94708, USA.
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3
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Grasso-Cladera A, Bremer M, Ladouce S, Parada F. A systematic review of mobile brain/body imaging studies using the P300 event-related potentials to investigate cognition beyond the laboratory. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:631-659. [PMID: 38834886 DOI: 10.3758/s13415-024-01190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2024] [Indexed: 06/06/2024]
Abstract
The P300 ERP component, related to the onset of task-relevant or infrequent stimuli, has been widely used in the Mobile Brain/Body Imaging (MoBI) literature. This systematic review evaluates the quality and breadth of P300 MoBI studies, revealing a maturing field with well-designed research yet grappling with standardization and global representation challenges. While affirming the reliability of measuring P300 ERP components in mobile settings, the review identifies significant hurdles in standardizing data cleaning and processing techniques, impacting comparability and reproducibility. Geographical disparities emerge, with studies predominantly in the Global North and a dearth of research from the Global South, emphasizing the need for broader inclusivity to counter the WEIRD bias in psychology. Collaborative projects and mobile EEG systems showcase the feasibility of reaching diverse populations, which is essential to advance precision psychiatry and to integrate varied data streams. Methodologically, a trend toward ecological validity is noted, shifting from lab-based to real-world settings with portable EEG system advancements. Future hardware developments are expected to balance signal quality and sensor intrusiveness, enriching data collection in everyday contexts. Innovative methodologies reflect a move toward more natural experimental settings, prompting critical questions about the applicability of traditional ERP markers, such as the P300 outside structured paradigms. The review concludes by highlighting the crucial role of integrating mobile technologies, physiological sensors, and machine learning to advance cognitive neuroscience. It advocates for an operational definition of ecological validity to bridge the gap between controlled experiments and the complexity of embodied cognitive experiences, enhancing both theoretical understanding and practical application in study design.
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Affiliation(s)
| | - Marko Bremer
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile
- Facultad de Psicología, Programa de Magíster en Neurociencia Social, Diego Portales University, Santiago, Chile
| | - Simon Ladouce
- Department Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Francisco Parada
- Facultad de Psicología, Centro de Estudios en Neurociencia Humana y Neuropsicología (CENHN), Diego Portales University, Santiago, Chile.
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4
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Crivelli D, Balconi M. From physical to digital: A theoretical-methodological primer on designing hyperscanning investigations to explore remote exchanges. Soc Neurosci 2024:1-9. [PMID: 39043222 DOI: 10.1080/17470919.2024.2380725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Indexed: 07/25/2024]
Abstract
As individuals increasingly engage in social interactions through digital mediums, understanding the neuroscientific underpinnings of such exchanges becomes a critical challenge and a valuable opportunity. In line with a second-person neuroscience approach, understanding the forms of interpersonal syntonisation that occur during digital interactions is pivotal for grasping the mechanisms underlying successful collaboration in virtual spaces. The hyperscanning paradigm, involving the simultaneous monitoring of the brains and bodies of multiple interacting individuals, seems to be a powerful tool for unravelling the neural correlates of interpersonal syntonisation in social exchanges. We posit that such approach can now open new windows on interacting brains' responses even to digitally-conveyed social cues, offering insights into how social information is processed in the absence of traditional face-to-face settings. Yet, such paradigm shift raises challenging methodological questions, which should be answered properly to conduct significant and informative hyperscanning investigations. Here, we provide an introduction to core methodological issues dedicated to novices approaching the design of hyperscanning investigations of remote exchanges in natural settings, focusing on the selection of neuroscientific devices, synchronization of data streams, and data analysis approaches. Finally, a methodological checklist for devising robust hyperscanning studies on digital interactions is presented.
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Affiliation(s)
- Davide Crivelli
- International Research Center for Cognitive Applied Neuroscience (IrcCAN), Faculty of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
- Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Michela Balconi
- International Research Center for Cognitive Applied Neuroscience (IrcCAN), Faculty of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
- Research Unit in Affective and Social Neuroscience, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
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5
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Cooke A, Hindle J, Lawrence C, Bellomo E, Pritchard AW, MacLeod CA, Martin-Forbes P, Jones S, Bracewell M, Linden DEJ, Mehler DMA. Effects of home-based EEG neurofeedback training as a non-pharmacological intervention for Parkinson's disease. Neurophysiol Clin 2024; 54:102997. [PMID: 38991470 DOI: 10.1016/j.neucli.2024.102997] [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: 03/21/2024] [Revised: 06/12/2024] [Accepted: 06/12/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVES Aberrant movement-related cortical activity has been linked to impaired motor function in Parkinson's disease (PD). Dopaminergic drug treatment can restore these, but dosages and long-term treatment are limited by adverse side-effects. Effective non-pharmacological treatments could help reduce reliance on drugs. This experiment reports the first study of home-based electroencephalographic (EEG) neurofeedback training as a non-pharmacological candidate treatment for PD. Our primary aim was to test the feasibility of our EEG neurofeedback intervention in a home setting. METHODS Sixteen people with PD received six home visits comprising symptomology self-reports, a standardised motor assessment, and a precision handgrip force production task while EEG was recorded (visits 1, 2 and 6); and 3 × 1-hr EEG neurofeedback training sessions to supress the EEG mu rhythm before initiating handgrip movements (visits 3 to 5). RESULTS Participants successfully learned to self-regulate mu activity, and this appeared to expedite the initiation of precision movements (i.e., time to reach target handgrip force off-medication pre-intervention = 628 ms, off-medication post-intervention = 564 ms). There was no evidence of wider symptomology reduction (e.g., Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III Motor Examination, off-medication pre-intervention = 29.00, off-medication post intervention = 30.07). Interviews indicated that the intervention was well-received. CONCLUSION Based on the significant effect of neurofeedback on movement-related cortical activity, positive qualitative reports from participants, and a suggestive benefit to movement initiation, we conclude that home-based neurofeedback for people with PD is a feasible and promising non-pharmacological treatment that warrants further research.
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Affiliation(s)
- Andrew Cooke
- Instutute for the Psychology of Elite Performance (IPEP), Bangor University, UK; School of Psychology and Sport Science, Bangor University, UK.
| | - John Hindle
- The Centre for Research in Ageing and Cognitive Health (REACH), University of Exeter, UK; University of Exeter Medical School, UK
| | - Catherine Lawrence
- Centre for Health Economics and Medicines Evaluation (CHEME), Bangor University, UK; School of Health Sciences, Bangor University, UK
| | - Eduardo Bellomo
- Instutute for the Psychology of Elite Performance (IPEP), Bangor University, UK
| | | | - Catherine A MacLeod
- Centre for Population Health Sciences, Usher Institute, The University of Edinburgh, UK
| | | | | | - Martyn Bracewell
- School of Psychology and Sport Science, Bangor University, UK; North Wales Medical School, Bangor University, UK; Walton Centre NHS Foundation Trust, UK
| | - David E J Linden
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, UK; MRC Center for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, UK
| | - David M A Mehler
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, UK; MRC Center for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, UK; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Germany; Institute for Translational Psychiatry, University Hospital Münster, Germany
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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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Chan MMY, Choi CXT, Tsoi TCW, Zhong J, Han YMY. Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study. Clin Neurophysiol 2024; 163:56-67. [PMID: 38703700 DOI: 10.1016/j.clinph.2024.04.004] [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: 07/06/2023] [Revised: 01/29/2024] [Accepted: 04/05/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology. METHODS A total of 83 ASD and typically developing (TD) individuals participated in this study. Participants' interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio. RESULTS ASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017). CONCLUSIONS Differential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation. SIGNIFICANCE Brain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.
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Affiliation(s)
- Melody M Y Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Queensland Brain Institute, The University of Queensland, St Lucia QLD 4072, Australia
| | - Coco X T Choi
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Tom C W Tsoi
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Junpei Zhong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Yvonne M Y Han
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; University Research Facility in Behavioral and Systems Neuroscience (UBSN), The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
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8
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Brandt-Rauf PW, Ayaz H. Occupational Health and Neuroergonomics: The Future of Wearable Neurotechnologies at the Workplace. J Occup Environ Med 2024; 66:456-460. [PMID: 38829949 DOI: 10.1097/jom.0000000000003080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Affiliation(s)
- Paul W Brandt-Rauf
- From the School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania
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9
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van den Hoek TC, van de Ruit M, Terwindt GM, Tolner EA. EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility? Brain Sci 2024; 14:508. [PMID: 38790486 PMCID: PMC11119734 DOI: 10.3390/brainsci14050508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024] Open
Abstract
Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.
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Affiliation(s)
- Thomas C. van den Hoek
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Mark van de Ruit
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Gisela M. Terwindt
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
| | - Else A. Tolner
- Department of Neurology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands (M.v.d.R.); (G.M.T.)
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
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10
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Konrad K, Gerloff C, Kohl SH, Mehler DMA, Mehlem L, Volbert EL, Komorek M, Henn AT, Boecker M, Weiss E, Reindl V. Interpersonal neural synchrony and mental disorders: unlocking potential pathways for clinical interventions. Front Neurosci 2024; 18:1286130. [PMID: 38529267 PMCID: PMC10962391 DOI: 10.3389/fnins.2024.1286130] [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: 08/30/2023] [Accepted: 01/30/2024] [Indexed: 03/27/2024] Open
Abstract
Introduction Interpersonal synchronization involves the alignment of behavioral, affective, physiological, and brain states during social interactions. It facilitates empathy, emotion regulation, and prosocial commitment. Mental disorders characterized by social interaction dysfunction, such as Autism Spectrum Disorder (ASD), Reactive Attachment Disorder (RAD), and Social Anxiety Disorder (SAD), often exhibit atypical synchronization with others across multiple levels. With the introduction of the "second-person" neuroscience perspective, our understanding of interpersonal neural synchronization (INS) has improved, however, so far, it has hardly impacted the development of novel therapeutic interventions. Methods To evaluate the potential of INS-based treatments for mental disorders, we performed two systematic literature searches identifying studies that directly target INS through neurofeedback (12 publications; 9 independent studies) or brain stimulation techniques (7 studies), following PRISMA guidelines. In addition, we narratively review indirect INS manipulations through behavioral, biofeedback, or hormonal interventions. We discuss the potential of such treatments for ASD, RAD, and SAD and using a systematic database search assess the acceptability of neurofeedback (4 studies) and neurostimulation (4 studies) in patients with social dysfunction. Results Although behavioral approaches, such as engaging in eye contact or cooperative actions, have been shown to be associated with increased INS, little is known about potential long-term consequences of such interventions. Few proof-of-concept studies have utilized brain stimulation techniques, like transcranial direct current stimulation or INS-based neurofeedback, showing feasibility and preliminary evidence that such interventions can boost behavioral synchrony and social connectedness. Yet, optimal brain stimulation protocols and neurofeedback parameters are still undefined. For ASD, RAD, or SAD, so far no randomized controlled trial has proven the efficacy of direct INS-based intervention techniques, although in general brain stimulation and neurofeedback methods seem to be well accepted in these patient groups. Discussion Significant work remains to translate INS-based manipulations into effective treatments for social interaction disorders. Future research should focus on mechanistic insights into INS, technological advancements, and rigorous design standards. Furthermore, it will be key to compare interventions directly targeting INS to those targeting other modalities of synchrony as well as to define optimal target dyads and target synchrony states in clinical interventions.
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Affiliation(s)
- Kerstin Konrad
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- JARA Brain Institute II, Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany
| | - Christian Gerloff
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- JARA Brain Institute II, Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany
- Department of Applied Mathematics and Theoretical Physics, Cambridge Centre for Data-Driven Discovery, University of Cambridge, Cambridge, United Kingdom
| | - Simon H. Kohl
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- JARA Brain Institute II, Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Jülich, Germany
| | - David M. A. Mehler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- School of Psychology, Cardiff University Brain Research Imaging Center (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Lena Mehlem
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
| | - Emily L. Volbert
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
| | - Maike Komorek
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
| | - Alina T. Henn
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
| | - Maren Boecker
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- Institute of Medical Psychology and Medical Sociology, University Hospital RWTH, Aachen, Germany
| | - Eileen Weiss
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- Institute of Medical Psychology and Medical Sociology, University Hospital RWTH, Aachen, Germany
| | - Vanessa Reindl
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH, Aachen, Germany
- Department of Psychology, School of Social Sciences, Nanyang Technological University, Singapore, Singapore
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Yu Z, Guo S. A low-cost, wireless, 4-channel EEG measurement system used in virtual reality environments. HARDWAREX 2024; 17:e00507. [PMID: 38327677 PMCID: PMC10847955 DOI: 10.1016/j.ohx.2024.e00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/20/2023] [Accepted: 01/19/2024] [Indexed: 02/09/2024]
Abstract
The combination of Virtual Reality (VR) technology and Electroencephalography (EEG) measurements has shown tremendous potential in the fields of psychology and neuroscience research. However, the majority of EEG measurement devices currently available are expensive, bulky, uncomfortable to wear, and difficult to integrate with VR headsets. These limitations have hindered the development of related research fields. This study describes a low-cost (60.07 USD), small-sized, wireless, high-precision, low-power consumption 4-channel EEG measurement system (NeuroVista) for frontal area EEG measurements, which can be used with a VR headset, enabling EEG measurements in VR environments. The system has an input-referred noise of less than 0.9480 μ V r m s , a common mode rejection ratio of over 96 dB, a measurement resolution of less than 0.1 μ V , a bandwidth of 0.5 ∼ 45 Hz, and works at a sampling rate of 250 Hz. It also supports metal dry electrodes and includes a built-in analog bandpass filter, right-leg drive circuit, and built-in digital lowpass filter and notch filter, which can reduce noise during measurement. Researchers can reconstruct the electrode system to measure regions of interest according to their needs.
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Affiliation(s)
- Zhiyuan Yu
- Department of Biomedical Engineering, School of Materials, South China University of Technology, Guangdong Province, China
| | - Shengwen Guo
- Department of Intelligent Science and Engineering, School of Automation, South China University of Technology, Guangdong Province, China
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12
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Jafarzadeh Esfahani M, Sikder N, Ter Horst R, Daraie AH, Appel K, Weber FD, Bevelander KE, Dresler M. Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat. Eur J Neurosci 2024; 59:948-965. [PMID: 38328991 DOI: 10.1111/ejn.16227] [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: 01/22/2023] [Revised: 11/09/2023] [Accepted: 11/30/2023] [Indexed: 02/09/2024]
Abstract
Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.
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Affiliation(s)
| | - Niloy Sikder
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, Kleve, Germany
| | - Rob Ter Horst
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Amir Hossein Daraie
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Frederik D Weber
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Kirsten E Bevelander
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
- Primary and Community Care, Radboud University and Medical Center, Nijmegen, The Netherlands
| | - Martin Dresler
- Donders Institute for Brain, Behaviour, and Cognition, Radboudumc, Nijmegen, The Netherlands
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13
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Kleeva D, Ninenko I, Lebedev MA. Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations. Front Neurosci 2024; 18:1326139. [PMID: 38370431 PMCID: PMC10873917 DOI: 10.3389/fnins.2024.1326139] [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: 10/22/2023] [Accepted: 01/05/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes. Methods Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications. Results Distinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality. Discussion We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system.
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Affiliation(s)
- Daria Kleeva
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Ivan Ninenko
- Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
| | - Mikhail A. Lebedev
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia
- I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Saint Petersburg, Russia
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14
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Özkurt TE. Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease. J Neurophysiol 2024; 131:435-445. [PMID: 38230880 DOI: 10.1152/jn.00272.2023] [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: 07/17/2023] [Revised: 11/29/2023] [Accepted: 01/11/2024] [Indexed: 01/18/2024] Open
Abstract
Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various β band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of β-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on β band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.
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Affiliation(s)
- Tolga Esat Özkurt
- Department of Health Informatics, Graduate School of Informatics, Middle East Technical University (METU), Ankara, Turkey
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15
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Nordli DR, Fives K, Galan F. Portable Headband Electroencephalogram in the Detection of Absence Epilepsy. Clin EEG Neurosci 2024:15500594241229153. [PMID: 38298021 DOI: 10.1177/15500594241229153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
The accuracy of headband electroencephalogram (EEG) was compared to traditional EEG in pediatric patients with absence epilepsy. This study enrolled 10 patients with previously diagnosed absence epilepsy and examined the concordance of headband EEG and traditional EEG in the follow-up EEG of treated absence epilepsy. The study found a concordant result in 80% of cases providing a signal that absence epilepsy is an effective target for headband EEG. The study showcases a need for further research in headband EEG technology and continued improvements in technology.
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Affiliation(s)
- Douglas R Nordli
- Department of Neurology, University of Chicago, Chicago, IL, USA
| | - Kaila Fives
- Lake Erie College of Osteopathic Medicine, Bradenton, FL, USA
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16
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Sadeghi M, Bristow T, Fakorede S, Liao K, Palmer JA, Lyons KE, Pahwa R, Huang CK, Akinwuntan A, Devos H. The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.26.24301687. [PMID: 38352617 PMCID: PMC10862999 DOI: 10.1101/2024.01.26.24301687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
Aims Balance requires the cortical control of visual, somatosensory, and vestibular inputs. The aim of this cross-sectional study was to compare the contributions of each of these systems on postural control and cortical activity using a sensory reweighting approach between participants with Parkinson's disease (PD) and controls. Methods Ten participants with PD (age: 72 ± 9; 3 women; Hoehn & Yahr: 2 [1.5 - 2.50]) and 11 controls (age: 70 ± 3; 4 women) completed a sensory organization test in virtual reality (VR-SOT) while cortical activity was being recorded using electroencephalography (EEG). Conditions 1 to 3 were completed on a stable platform; conditions 4 to 6 on a foam. Conditions 1 and 4 were done with eyes open; conditions 2 and 5 in a darkened VR environment; and conditions 3 and 6 in a moving VR environment. Linear mixed models were used to evaluate changes in center of pressure (COP) displacement and EEG alpha and theta/beta ratio power between the two groups across the postural control conditions. Condition 1 was used as reference in all analyses. Results Participants with PD showed greater COP displacement than controls in the anteroposterior (AP) direction when relying on vestibular input (condition 5; p<0.0001). The mediolateral (ML) COP sway was greater in PD than in controls when relying on the somatosensory (condition 2; p = 0.03), visual (condition 4; p = 0.002), and vestibular (condition 5; p < 0.0001) systems. Participants with PD exhibited greater alpha power compared to controls when relying on visual input (condition 2; p = 0.003) and greater theta/beta ratio power when relying on somatosensory input (condition 4; p = 0.001). Conclusions PD affects reweighting of postural control, exemplified by greater COP displacement and increased cortical activity. Further research is needed to establish the temporal dynamics between cortical activity and COP displacement.
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17
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Ulate-Campos A, Loddenkemper T. Review on the current long-term, limited lead electroencephalograms. Epilepsy Behav 2024; 150:109557. [PMID: 38070411 DOI: 10.1016/j.yebeh.2023.109557] [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] [Received: 10/10/2023] [Revised: 11/14/2023] [Accepted: 11/17/2023] [Indexed: 01/14/2024]
Abstract
In the last century, 10-20 lead EEG recordings became the gold standard of surface EEG recordings, and the 10-20 system provided comparability between international studies. With the emergence of advanced EEG sensors, that may be able to record and process signals in much more compact units, this additional sensor technology now opens up opportunities to revisit current ambulatory EEG recording practices and specific patient populations, and even electrodes that are embedded into the head surface. Here, we aim to provide an overview of current limited sensor long-term EEG systems. We performed a literature review using Pubmed as a database and included the relevant articles. The review identified several systems for recording long-term ambulatory EEGs. In general, EEGs recorded with these modalities can be acquired in ambulatory and home settings, achieve good sensitivity with low false detection rates, are used for automatic seizure detection as well as seizure forecasting, and are well tolerated by patients, but each of them has advantages and disadvantages. Subcutaneous, subgaleal, and subscalp electrodes are minimally invasive and provide stable signals that can record ultra--long-term EEG and are in general less noisy than scalp EEG, but they have limited spatial coverage and require anesthesia, a surgical procedure and a trained surgeon to be placed. Behind and in the ear electrodes are discrete, unobtrusive with a good sensitivity mainly for temporal seizures but might miss extratemporal seizures, recordings could be obscured by muscle artifacts and bilateral ictal patterns might be difficult to register. Finally, recording systems using electrodes in a headband can be easily and quickly placed by the patient or caregiver, but have less spatial coverage and are more prone to movement because electrodes are not attached. Overall, limited EEG recording systems offer a promising opportunity to potentially record targeted EEG with focused indications for prolonged periods, but further validation work is needed.
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18
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Kaltsouni E, Schmidt F, Zsido RG, Eriksson A, Sacher J, Sundström-Poromaa I, Sumner RL, Comasco E. Electroencephalography findings in menstrually-related mood disorders: A critical review. Front Neuroendocrinol 2024; 72:101120. [PMID: 38176542 DOI: 10.1016/j.yfrne.2023.101120] [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] [Received: 05/09/2023] [Revised: 12/21/2023] [Accepted: 12/31/2023] [Indexed: 01/06/2024]
Abstract
The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being luteal-phase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.
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Affiliation(s)
- Elisavet Kaltsouni
- Department of Womeńs and Childreńs Health, Science for Life Laboratory, Uppsala University, Sweden
| | - Felix Schmidt
- Department of Womeńs and Childreńs Health, Science for Life Laboratory, Uppsala University, Sweden; Centre for Women's Mental Health during the Reproductive Lifespan, Uppsala University, 751 85 Uppsala, Sweden
| | - Rachel G Zsido
- Cognitive Neuroendocrinology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Department of Psychiatry, Clinical Neuroscience Laboratory for Sex Differences in the Brain, Massachusetts General Hospital, Harvard Medical School, USA
| | - Allison Eriksson
- Centre for Women's Mental Health during the Reproductive Lifespan, Uppsala University, 751 85 Uppsala, Sweden; Department of Womeńs and Childreńs Health, Uppsala University, Sweden
| | - Julia Sacher
- Cognitive Neuroendocrinology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Clinic of Cognitive Neurology, University of Leipzig, Germany
| | | | | | - Erika Comasco
- Department of Womeńs and Childreńs Health, Science for Life Laboratory, Uppsala University, Sweden.
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19
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Vukelić M, Bui M, Vorreuther A, Lingelbach K. Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment. FRONTIERS IN NEUROERGONOMICS 2023; 4:1274730. [PMID: 38234482 PMCID: PMC10790930 DOI: 10.3389/fnrgo.2023.1274730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/31/2023] [Indexed: 01/19/2024]
Abstract
Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.
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Affiliation(s)
- Mathias Vukelić
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany
| | - Michael Bui
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany
| | - Anna Vorreuther
- Applied Neurocognitive Systems, Institute of Human Factors and Technology Management (IAT), University of Stuttgart, Stuttgart, Germany
| | - Katharina Lingelbach
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering (IAO), Stuttgart, Germany
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20
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Simmatis L, Russo EE, Geraci J, Harmsen IE, Samuel N. Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder. NPJ MENTAL HEALTH RESEARCH 2023; 2:18. [PMID: 38609518 PMCID: PMC10955915 DOI: 10.1038/s44184-023-00038-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/21/2023] [Indexed: 04/14/2024]
Abstract
Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.
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Affiliation(s)
- Leif Simmatis
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Emma E Russo
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Joseph Geraci
- Cove Neurosciences Inc., Toronto, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Irene E Harmsen
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Nardin Samuel
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Cove Neurosciences Inc., Toronto, ON, Canada.
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21
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Di Flumeri G, Giorgi A, Germano D, Ronca V, Vozzi A, Borghini G, Tamborra L, Simonetti I, Capotorto R, Ferrara S, Sciaraffa N, Babiloni F, Aricò P. A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees. SENSORS (BASEL, SWITZERLAND) 2023; 23:8389. [PMID: 37896483 PMCID: PMC10610858 DOI: 10.3390/s23208389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.
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Affiliation(s)
- Gianluca Di Flumeri
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Andrea Giorgi
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Daniele Germano
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Vincenzo Ronca
- BrainSigns srl, 00198 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
| | - Alessia Vozzi
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Gianluca Borghini
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
| | - Luca Tamborra
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Ilaria Simonetti
- BrainSigns srl, 00198 Rome, Italy
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Rossella Capotorto
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | | | | | - Fabio Babiloni
- Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
- BrainSigns srl, 00198 Rome, Italy
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Pietro Aricò
- BrainSigns srl, 00198 Rome, Italy
- Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, 00185 Rome, Italy
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22
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Alsuradi H, Park W, Eid M. An ensemble deep-learning approach for single-trial EEG classification of vibration intensity. J Neural Eng 2023; 20:056027. [PMID: 37732958 DOI: 10.1088/1741-2552/acfbf9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/21/2023] [Indexed: 09/22/2023]
Abstract
Objective. Single-trial electroencephalography (EEG) classification is a promising approach to evaluate the cognitive experience associated with haptic feedback. Convolutional neural networks (CNNs), which are among the most widely used deep learning techniques, have demonstrated their effectiveness in extracting EEG features for the classification of different cognitive functions, including the perception of vibration intensity that is often experienced during human-computer interaction. This paper proposes a novel CNN ensemble model to classify the vibration-intensity from a single trial EEG data that outperforms the state-of-the-art EEG models.Approach. The proposed ensemble model, named SE NexFusion, builds upon the observed complementary learning behaviors of the EEGNex and TCNet Fusion models, exhibited in learning personal as well generic neural features associated with vibration intensity. The proposed ensemble employs multi-branch feature encoders corroborated with squeeze-and-excitation units that enables rich-feature encoding while at the same time recalibrating the weightage of the obtained feature maps based on their discriminative power. The model takes in a single trial of raw EEG as an input and does not require complex EEG signal-preprocessing.Main results. The proposed model outperforms several state-of-the-art bench-marked EEG models by achieving an average accuracy of 60.7% and 61.6% under leave-one-subject-out and within-subject cross-validation (three-classes), respectively. We further validate the robustness of the model through Shapley values explainability method, where the most influential spatio-temporal features of the model are counter-checked with the neural correlates that encode vibration intensity.Significance. Results show that SE NexFusion outperforms other benchmarked EEG models in classifying the vibration intensity. Additionally, explainability analysis confirms the robustness of the model in attending to features associated with the neural correlates of vibration intensity.
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Affiliation(s)
- Haneen Alsuradi
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
| | - Wanjoo Park
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
| | - Mohamad Eid
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
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23
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Delnatte C, Roze E, Pouget P, Galléa C, Welniarz Q. Can neuroscience enlighten the philosophical debate about free will? Neuropsychologia 2023; 188:108632. [PMID: 37385373 DOI: 10.1016/j.neuropsychologia.2023.108632] [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: 02/15/2023] [Revised: 06/24/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023]
Abstract
Free will has been at the heart of philosophical and scientific discussions for many years. However, recent advances in neuroscience have been perceived as a threat to the commonsense notion of free will as they challenge two core requirements for actions to be free. The first is the notion of determinism and free will, i.e., decisions and actions must not be entirely determined by antecedent causes. The second is the notion of mental causation, i.e., our mental state must have causal effects in the physical world, in other words, actions are caused by conscious intention. We present the classical philosophical positions related to determinism and mental causation, and discuss how neuroscience could shed a new light on the philosophical debate based on recent experimental findings. Overall, we conclude that the current evidence is insufficient to undermine free will.
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Affiliation(s)
| | - Emmanuel Roze
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS UMR 7225, Paris Brain Institute Institut du Cerveau, F-75013, Paris, France; Assistance Publique - Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Département de Neurologie, Paris, France
| | - Pierre Pouget
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS UMR 7225, Paris Brain Institute Institut du Cerveau, F-75013, Paris, France
| | - Cécile Galléa
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS UMR 7225, Paris Brain Institute Institut du Cerveau, F-75013, Paris, France
| | - Quentin Welniarz
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS UMR 7225, Paris Brain Institute Institut du Cerveau, F-75013, Paris, France.
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Osama M, Ateya AA, Sayed MS, Hammad M, Pławiak P, Abd El-Latif AA, Elsayed RA. Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions. SENSORS (BASEL, SWITZERLAND) 2023; 23:7435. [PMID: 37687891 PMCID: PMC10490658 DOI: 10.3390/s23177435] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/15/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023]
Abstract
Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.
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Affiliation(s)
- Manar Osama
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
| | - Abdelhamied A. Ateya
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
| | - Mohammed S. Sayed
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
- Department of Electronics and Communication Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
| | - Mohamed Hammad
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
- Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El Kom 32511, Egypt
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
| | - Ahmed A. Abd El-Latif
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia; (M.H.); (A.A.A.E.-L.)
- Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shibin El Kom 32511, Egypt
| | - Rania A. Elsayed
- Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt; (M.O.); (M.S.S.); (R.A.E.)
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25
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Morrel J, Singapuri K, Landa RJ, Reetzke R. Neural correlates and predictors of speech and language development in infants at elevated likelihood for autism: a systematic review. Front Hum Neurosci 2023; 17:1211676. [PMID: 37662636 PMCID: PMC10469683 DOI: 10.3389/fnhum.2023.1211676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is an increasingly prevalent and heterogeneous neurodevelopmental condition, characterized by social communicative differences, and a combination of repetitive behaviors, focused interests, and sensory sensitivities. Early speech and language delays are characteristic of young autistic children and are one of the first concerns reported by parents; often before their child's second birthday. Elucidating the neural mechanisms underlying these delays has the potential to improve early detection and intervention efforts. To fill this gap, this systematic review aimed to synthesize evidence on early neurobiological correlates and predictors of speech and language development across different neuroimaging modalities in infants with and without a family history of autism [at an elevated (EL infants) and low likelihood (LL infants) for developing autism, respectively]. A comprehensive, systematic review identified 24 peer-reviewed articles published between 2012 and 2023, utilizing structural magnetic resonance imaging (MRI; n = 2), functional MRI (fMRI; n = 4), functional near-infrared spectroscopy (fNIRS; n = 4), and electroencephalography (EEG; n = 14). Three main themes in results emerged: compared to LL infants, EL infants exhibited (1) atypical language-related neural lateralization; (2) alterations in structural and functional connectivity; and (3) mixed profiles of neural sensitivity to speech and non-speech stimuli, with some differences detected as early as 6 weeks of age. These findings suggest that neuroimaging techniques may be sensitive to early indicators of speech and language delays well before overt behavioral delays emerge. Future research should aim to harmonize experimental paradigms both within and across neuroimaging modalities and additionally address the feasibility, acceptability, and scalability of implementing such methodologies in non-academic, community-based settings.
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Affiliation(s)
- Jessica Morrel
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Kripi Singapuri
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Rebecca J. Landa
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Rachel Reetzke
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
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26
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Gado S, Lingelbach K, Wirzberger M, Vukelić M. Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:6546. [PMID: 37514840 PMCID: PMC10383122 DOI: 10.3390/s23146546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.
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Affiliation(s)
- Sabrina Gado
- Experimental Clinical Psychology, Department of Psychology, Julius-Maximilians-University of Würzburg, 97070 Würzburg, Germany
| | - Katharina Lingelbach
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, 70569 Stuttgart, Germany
- Applied Neurocognitive Psychology Lab, Department of Psychology, Carl von Ossietzky University, 26129 Oldenburg, Germany
| | - Maria Wirzberger
- Department of Teaching and Learning with Intelligent Systems, University of Stuttgart, 70174 Stuttgart, Germany
- LEAD Graduate School & Research Network, University of Tübingen, 72072 Tübingen, Germany
| | - Mathias Vukelić
- Applied Neurocognitive Systems, Fraunhofer Institute for Industrial Engineering IAO, 70569 Stuttgart, Germany
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27
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Liu C, Zhang C, Sun L, Liu K, Liu H, Zhu W, Jiang C. Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1035. [PMID: 37509982 PMCID: PMC10378707 DOI: 10.3390/e25071035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 06/25/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.
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Affiliation(s)
- Chenglin Liu
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
| | - Chenyang Zhang
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
| | - Luohao Sun
- School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
| | - Kun Liu
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
| | - Haiyue Liu
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
| | - Wenbing Zhu
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
| | - Chaozhe Jiang
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China
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28
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Marcantoni I, Assogna R, Del Borrello G, Di Stefano M, Morano M, Romagnoli S, Leoni C, Bruschi G, Sbrollini A, Morettini M, Burattini L. Ratio Indexes Based on Spectral Electroencephalographic Brainwaves for Assessment of Mental Involvement: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5968. [PMID: 37447818 DOI: 10.3390/s23135968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/18/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND This review systematically examined the scientific literature about electroencephalogram-derived ratio indexes used to assess human mental involvement, in order to deduce what they are, how they are defined and used, and what their best fields of application are. (2) Methods: The review was carried out according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. (3) Results: From the search query, 82 documents resulted. The majority (82%) were classified as related to mental strain, while 12% were classified as related to sensory and emotion aspects, and 6% to movement. The electroencephalographic electrode montage used was low-density in 13%, high-density in 6% and very-low-density in 81% of documents. The most used electrode positions for computation of involvement indexes were in the frontal and prefrontal cortex. Overall, 37 different formulations of involvement indexes were found. None of them could be directly related to a specific field of application. (4) Conclusions: Standardization in the definition of these indexes is missing, both in the considered frequency bands and in the exploited electrodes. Future research may focus on the development of indexes with a unique definition to monitor and characterize mental involvement.
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Affiliation(s)
- Ilaria Marcantoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Raffaella Assogna
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Giulia Del Borrello
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Marina Di Stefano
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Martina Morano
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Sofia Romagnoli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Chiara Leoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Micaela Morettini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy
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Craik A, González-España JJ, Alamir A, Edquilang D, Wong S, Sánchez Rodríguez L, Feng J, Francisco GE, Contreras-Vidal JL. Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface. SENSORS (BASEL, SWITZERLAND) 2023; 23:5930. [PMID: 37447780 DOI: 10.3390/s23135930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.
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Affiliation(s)
- Alexander Craik
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Juan José González-España
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Ayman Alamir
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
- Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia
| | - David Edquilang
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Sarah Wong
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Lianne Sánchez Rodríguez
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Jeff Feng
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Gerard E Francisco
- Department of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USA
- The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA
| | - Jose L Contreras-Vidal
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
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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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31
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López-Larraz E, Escolano C, Robledo-Menéndez A, Morlas L, Alda A, Minguez J. A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles. Front Hum Neurosci 2023; 17:1135153. [PMID: 37305362 PMCID: PMC10250743 DOI: 10.3389/fnhum.2023.1135153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/20/2023] [Indexed: 06/13/2023] Open
Abstract
This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.
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Tan X, Wang D, Chen J, Xu M. Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification. Bioengineering (Basel) 2023; 10:bioengineering10050609. [PMID: 37237679 DOI: 10.3390/bioengineering10050609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.
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Affiliation(s)
- Xiyue Tan
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Dan Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jiaming Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Meng Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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33
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Wu YC, Maymon C, Paden J, Liu W. Launching Your VR Neuroscience Laboratory. Curr Top Behav Neurosci 2023; 65:25-46. [PMID: 37306851 DOI: 10.1007/7854_2023_420] [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
The proliferation and refinement of affordable virtual reality (VR) technologies and wearable sensors have opened new frontiers in cognitive and behavioral neuroscience. This chapter offers a broad overview of VR for anyone interested in leveraging it as a research tool. In the first section, it examines the fundamental functionalities of VR and outlines important considerations that inform the development of immersive content that stimulates the senses. In the second section, the focus of the discussion shifts to the implementation of VR in the context of the neuroscience lab. Practical advice is offered on adapting commercial, off-the-shelf devices to a researcher's specific purposes. Further, methods are explored for recording, synchronizing, and fusing heterogeneous forms of data obtained through the VR system or add-on sensors, as well as for labeling events and capturing game play. The reader should come away with an understanding of fundamental considerations that need to be addressed in order to launch a successful VR neuroscience research program.
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Affiliation(s)
- Ying Choon Wu
- University of California San Diego, San Diego, CA, USA
| | | | | | - Weichen Liu
- University of California San Diego, San Diego, CA, USA
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