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Zhou H, Yin L, Su R, Zhang Y, Yuan Y, Xie P, Li X. STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108123. [PMID: 38471292 DOI: 10.1016/j.cmpb.2024.108123] [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: 09/19/2023] [Revised: 11/28/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.
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Affiliation(s)
- Hao Zhou
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Liyong Yin
- The First Hospital of Qinhuangdao, Qinhuangdao, PR China
| | - Rui Su
- Hebei Medical University, Shijiazhuang, PR China
| | - Ying Zhang
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.
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Song BG, Kang N. Removal of movement artifacts and assessment of mental stress analyzing electroencephalogram of non-driving passengers under whole-body vibration. Front Neurosci 2024; 18:1328704. [PMID: 38726034 PMCID: PMC11079143 DOI: 10.3389/fnins.2024.1328704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/12/2024] [Indexed: 05/12/2024] Open
Abstract
The discomfort caused by whole-body vibration (WBV) has long been assessed using subjective surveys or objective measurements of body acceleration. However, surveys have the disadvantage that some of participants often express their feelings in a capricious manner, and acceleration data cannot take into account individual preferences and experiences of their emotions. In this study, we investigated vibration-induced mental stress using the electroencephalogram (EEG) of 22 seated occupants excited by random vibrations. Between the acceleration and the EEG signal, which contains electrical noise due to the head shaking caused by random vibrations, we found that there was a strong correlation, which acts as an artifact in the EEG, and therefore we removed it using an adaptive filter. After removing the artifact, we analyzed the characteristics of the brainwaves using topographic maps and observed that the activities detected in the frontal electrodes showed significant differences between the static and vibration conditions. Further, frontal alpha asymmetry (FAA) and relative band power indices in the frontal electrodes were analyzed statistically to assess mental stress under WBV. As the vibration level increased, EEG analysis in the frontal electrodes showed a decrease in FAA and alpha power but an increase in gamma power. These results are in good agreement with the literature in the sense that FAA and alpha band power decreases with increasing stress, thus demonstrating that WBV causes mental stress and that the stress increases with the vibration level. EEG assessment of stress during WBV is expected to be used in the evaluation of ride comfort alongside existing self-report and acceleration methods.
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Affiliation(s)
- Byoung-Gyu Song
- Department of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Namcheol Kang
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [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: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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Katmah R, Shehhi AA, Jelinek HF, Hulleck AA, Khalaf K. A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4189-4202. [PMID: 37847624 DOI: 10.1109/tnsre.2023.3325215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
BACKGROUND Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools. OBJECTIVE This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models. METHOD In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms. RESULTS The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous. CONCLUSION The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.
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Downey RJ, Ferris DP. iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:8214. [PMID: 37837044 PMCID: PMC10574843 DOI: 10.3390/s23198214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.
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Affiliation(s)
| | - Daniel P. Ferris
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA;
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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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Ortiz M, de la Ossa L, Juan J, Iáñez E, Torricelli D, Tornero J, Azorín JM. An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton. Sci Data 2023; 10:343. [PMID: 37268619 DOI: 10.1038/s41597-023-02243-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/16/2023] [Indexed: 06/04/2023] Open
Abstract
One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases that provide electroencephalography (EEG) data during the use of a lower-limb exoskeleton. The current paper presents a database designed with an experimental protocol aiming to assess not only motor imagery during the control of the device, but also the attention to gait on flat and inclined surfaces. The research was conducted as an EUROBENCH subproject in the facilities sited in Hospital Los Madroños, Brunete (Madrid). The data validation reaches accuracies over 70% in the assessment of motor imagery and attention to gait, which marks the present database as a valuable resource for researches interested on developing and testing new EEG-based BMIs.
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Affiliation(s)
- Mario Ortiz
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, 03202, Spain.
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, 03202, Spain.
| | - Luis de la Ossa
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, 03202, Spain
| | - Javier Juan
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, 03202, Spain
- Center for Clinical Neuroscience, Hospital los Madroños, Brunete (Madrid), Madrid, 28690, Spain
| | - Eduardo Iáñez
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, 03202, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, 03202, Spain
| | - Diego Torricelli
- Instituto Cajal, Spanish National Research Council (CSIC), Madrid, 28002, Spain
| | - Jesús Tornero
- Center for Clinical Neuroscience, Hospital los Madroños, Brunete (Madrid), Madrid, 28690, Spain
| | - José M Azorín
- Brain-Machine Interface System Lab, Miguel Hernández University of Elche, Elche, 03202, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, 03202, Spain
- Valencian Graduate School and Research Network of Artificial Intelligence - valgrAI, Valencia, Spain
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Gonsisko CB, Ferris DP, Downey RJ. iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:928. [PMID: 36679726 PMCID: PMC9863946 DOI: 10.3390/s23020928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.
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Colucci A, Vermehren M, Cavallo A, Angerhöfer C, Peekhaus N, Zollo L, Kim WS, Paik NJ, Soekadar SR. Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not? Neurorehabil Neural Repair 2022; 36:747-756. [PMID: 36426541 PMCID: PMC9720703 DOI: 10.1177/15459683221138751] [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] [Indexed: 11/27/2022]
Abstract
The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that-upon repeated use over several weeks-B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.
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Affiliation(s)
- Annalisa Colucci
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Mareike Vermehren
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Alessia Cavallo
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Cornelius Angerhöfer
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Niels Peekhaus
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany
| | - Loredana Zollo
- Unit of Advanced Robotics and Human-Centred Technologies (CREO Lab), University Campus Bio-Medico of Rome, Roma RM, Italy
| | - Won-Seok Kim
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Nam-Jong Paik
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Neurowissenschaftliches Forschungszentrum (NWFZ), Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité – Universitätsmedizin Berlin, Charitéplatz 1, Berlin, Germany,Surjo R. Soekadar, Charité Universitatsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany.
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Agounad S, Hamou S, Tarahi O, Moufassih M, Islam MK. Intelligent fuzzy system for automatic artifact detection and removal from EEG signals. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Studnicki A, Downey RJ, Ferris DP. Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155867. [PMID: 35957423 PMCID: PMC9371038 DOI: 10.3390/s22155867] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 05/27/2023]
Abstract
Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.
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A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features. Phys Eng Sci Med 2022; 45:705-719. [DOI: 10.1007/s13246-022-01135-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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Gorjan D, Gramann K, De Pauw K, Marusic U. Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng 2022; 19. [PMID: 35147512 DOI: 10.1088/1741-2552/ac542c] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/08/2022] [Indexed: 11/12/2022]
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.
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Affiliation(s)
- Dasa Gorjan
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
| | - Klaus Gramann
- Technische Universität Berlin, Fasanenstr. 1, Berlin, Berlin, 10623, GERMANY
| | - Kevin De Pauw
- Vrije Universiteit Brussel, Pleinlaan 2, Brussel, Brussel, 1050, BELGIUM
| | - Uros Marusic
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
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14
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Aslan Z, Akin M. A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med 2021; 45:83-96. [PMID: 34822131 DOI: 10.1007/s13246-021-01083-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/15/2021] [Indexed: 01/07/2023]
Abstract
This study presents a method with high accuracy performance that aims to automatically detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related literature studies using traditional machine learning algorithms, the features required for the training of the network are automatically extracted from the EEG records in our method. In order to obtain the time frequency features of the EEG signals, the signal was converted into 2D by using the Continuous Wavelet Transform method. This study has the highest accuracy performance in the relevant literature by using 2D time frequency features in automatic detection of SZ disease. It is trained with Visual Geometry Group-16 (VGG16), an advanced convolutional neural networks (CNN) deep learning network architecture, to extract key features found on scalogram images and train the network. The study shows a high success in classifying SZ patients and healthy individuals with a very satisfactory accuracy of 98% and 99.5%, respectively, using two different datasets consisting of individuals from different age groups. Using different techniques [Activization Maximization, Saliency Map, and Gradient-weighted Class Activation Mapping (Grad-CAM)] to visualize the learning outcomes of the CNN network, the relationship of frequency components between SZ and the healthy individual is clearly shown. Moreover, with these interpretable outcomes, the difference between SZ patients and healthy individuals can be distinguished very easily help for expert opinion.
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Affiliation(s)
- Zülfikar Aslan
- Technical Sciences Vocational School, Gaziantep University, Gaziantep, 27310, Turkey.
| | - Mehmet Akin
- Electrical-Electronics Engineering, Faculty of Engineering, Dicle University, Diyarbakir, 21280, Turkey
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15
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Steele AG, Parekh S, Azgomi HF, Ahmadi MB, Craik A, Pati S, Francis JT, Contreras-Vidal JL, Faghih RT. A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2037-2045. [PMID: 34543199 PMCID: PMC8626138 DOI: 10.1109/tnsre.2021.3113888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroencephalography (EEG) is a good candidate for seizure tracking. In this research, we make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, we produce a single seizure state estimation made up of these individual estimations. Using a modified wrapper feature selection, we determine two optimal features of mixed data type, one continuous and one binary analyzing all available channels. These features are used in a state-space framework to model the continuous hidden seizure state. Expectation maximization is performed offline on the training and validation data sets to estimate unknown parameters. The seizure state estimation process is performed for multiple channels, and the seizure state estimation is derived using a square-root Kalman filter. A second expectation maximization step is utilized to estimate the unknown square-root Kalman filter parameters. This method is tested in a real-time applicable way for seizure state estimation. Applying this approach, we obtain a single seizure state estimation with quantitative information about the likelihood of a seizure occurring, which we call seizure probability. Our results on the experimental data (CHB-MIT EEG database) validate the proposed estimation method and we achieve an average accuracy, sensitivity, and specificity of 92.7%, 92.8%, and 93.4%, respectively. The potential applications of this seizure estimation model are for closed-loop neuromodulation and long-term quantitative analysis of seizure treatment efficacy.
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16
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Kora P, Meenakshi K, Swaraja K, Rajani A, Raju MS. EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complement Ther Clin Pract 2021; 43:101329. [PMID: 33618287 DOI: 10.1016/j.ctcp.2021.101329] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/19/2021] [Accepted: 02/02/2021] [Indexed: 01/20/2023]
Abstract
OBJECTIVES The present investigation is to study the impact of yoga and meditation on Brain waves concerning physical and mental health. There are mainly three stages (steps) in the brain wave classification:(i) preprocessing, ii) feature extraction, and iii) classification. This work provides a review of interpretation methods of Brain signals (Electroencephalogram (EEG)) EEG during yoga and meditation. Past research has revealed significant mental and physical advantages with yoga and meditation. METHODS The research topic reviewed focused on the machine learning strategies applied for the interpretation of brain waves. In addressing the research questions highlighted earlier in the general introduction, we conducted a systematic search of articles from targeted scientific and journal online databases that included PubMed, Web of Science, IEEE Xplore Digital Library (IEEE), and Arxiv databases based on their relevance to the research questions and domain topic. The survey topic is relatively nascent, and therefore, the scope of the search period was limited to the 20-year timeline that was deemed representative of the research topic under investigation. The literature search was based on the keywords "EEG", "yoga*" and "meditation*". The key phrases were concatenated using Boolean expressions and applied to search through the selected online databases yielding a total of 120 articles. The online databases were selected based on the relevancy of content with the research title, research questions, and the domain application. The literature review search, process, and classification were carefully conducted guided by two defined measures; 1.) Inclusion criteria; and 2.) Exclusion criteria. These measures define the criteria for searching and extracting relevant articles relating to the research title and domain of interest. RESULTS Our literature search and review indicate a broad spectrum of neural mechanics under a variety of meditation styles have been investigated. A detailed analysis of various mental states using Zen, CHAN, mindfulness, TM, Rajayoga, Kundalini, Yoga, and other meditation styles have been described by means of EEG bands. Classification of mental states using KNN, SVM, Random forest, Fuzzy logic, neural networks, Convolutional Neural Networks has been described. Superior research is still required to classify the EEG signatures corresponding to different mental states. CONCLUSIONS Yoga practice may be an effective adjunctive treatment for a clinical and aging population. Advanced research can examine the effects of specific branches of yoga on a designated clinical grouping. Yoga and meditation increased overall healthy brain activity.
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Nahmias DO, Kontson KL. Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts. Front Neurosci 2021; 15:566004. [PMID: 33642972 PMCID: PMC7906969 DOI: 10.3389/fnins.2021.566004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 01/12/2021] [Indexed: 11/13/2022] Open
Abstract
With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets (N = 60 subjects) to score EEG quality based on the presence of ocular artifacts with our unimodal method and motion artifacts with our multimodal method. Further, we apply our unimodal source method to compare the performance of two different artifact removal algorithms. Our results show we are able to effectively score EEG data using both methods and apply our method to evaluate the performance of other artifact removal algorithms that target ocular artifacts. Methods developed and validated here can be used to assess data quality and evaluate the effectiveness of certain noise-reduction algorithms.
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Affiliation(s)
- David O. Nahmias
- Office of Science and Engineering Laboratories, Division of Biomedical Physics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States
| | - Kimberly L. Kontson
- Office of Science and Engineering Laboratories, Division of Biomedical Physics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
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18
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Lee YE, Kwak NS, Lee SW. A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2021; 28:2660-2670. [PMID: 33232242 DOI: 10.1109/tnsre.2020.3040264] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Recently, practical brain-computer interfaces (BCIs) have been widely investigated for detecting human intentions in real world. However, performance differences still exist between the laboratory and the real world environments. One of the main reasons for such differences comes from the user's unstable physical states (e.g., human movements are not strictly controlled), which produce unexpected signal artifacts. Hence, to minimize the performance degradation of electroencephalography (EEG)-based BCIs, we present a novel artifact removal method named constrained independent component analysis with online learning (cIOL). The cIOL can find and reject the noise-like components related to human body movements (i.e., movement artifacts) in the EEG signals. To obtain movement information, isolated electrodes are used to block electrical signals from the brain using high-resistance materials. We estimate artifacts with movement information using constrained independent component analysis from EEG signals and then extract artifact-free signals using online learning in each sample. In addition, the cIOL is evaluated by signal processing under 16 different experimental conditions (two types of EEG devices × two BCI paradigms × four different walking speeds). The experimental results show that the cIOL has the highest accuracy in both scalp- and ear-EEG, and has the highest signal-to-noise ratio in scalp-EEG among the state-of-the-art methods, except for the case of steady-state visual evoked potential at 2.0 m/s with superposition problem.
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19
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Baniqued PDE, Stanyer EC, Awais M, Alazmani A, Jackson AE, Mon-Williams MA, Mushtaq F, Holt RJ. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J Neuroeng Rehabil 2021; 18:15. [PMID: 33485365 PMCID: PMC7825186 DOI: 10.1186/s12984-021-00820-8] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 01/12/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective. METHODS A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. RESULTS 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. CONCLUSION 19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.
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Affiliation(s)
| | - Emily C Stanyer
- School of Psychology, University of Leeds, Leeds, LS2 9JZ, UK
| | - Muhammad Awais
- School of Psychology, University of Leeds, Leeds, LS2 9JZ, UK
| | - Ali Alazmani
- School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Andrew E Jackson
- School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | | | - Faisal Mushtaq
- School of Psychology, University of Leeds, Leeds, LS2 9JZ, UK.
| | - Raymond J Holt
- School of Mechanical Engineering, University of Leeds, Leeds, LS2 9JT, UK
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20
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Paek AY, Kilicarslan A, Korenko B, Gerginov V, Knappe S, Contreras-Vidal JL. Towards a Portable Magnetoencephalography Based Brain Computer Interface with Optically-Pumped Magnetometers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3420-3423. [PMID: 33018738 DOI: 10.1109/embc44109.2020.9176159] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain Computer Interfaces (BCIs) allow individuals to control devices, machines and prostheses with their thoughts. Most feasibility studies with BCIs have utilized scalp electroencephalography (EEG), due to it being accessible, noninvasive, and portable. While BCIs have been studied with magnetoencephalography (MEG), the modality has limited applications due to the large immobile hardware. Here we propose that room-temperature, optically-pumped magnetometers (OPMs) can potentially serve a portable modality that can be used for BCIs. OPMs have the added advantage that low-frequency neuromagnetic fields are not affected by volume conduction, which is known to distort EEG signals. In this feasibility study, we tested an OPM system with a real-time BCI where able bodied participants controlled a cursor to reach two targets. This BCI system used alpha and beta-band power modulations associated with hand movements. Our preliminary results show significant alpha and beta-band desynchronization due to movement, as found in previous literature.
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21
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Jacobsen NSJ, Blum S, Witt K, Debener S. A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings. Eur J Neurosci 2020; 54:8421-8440. [PMID: 32909315 DOI: 10.1111/ejn.14965] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 01/22/2023]
Abstract
Brain activity during natural walking outdoors can be captured using mobile electroencephalography (EEG). However, EEG recorded during gait is confounded with artifacts from various sources, possibly obstructing the interpretation of brain activity patterns. Currently, there is no consensus on how the amount of artifact present in these recordings should be quantified, or is there a systematic description of gait artifact properties. In the current study, we expand several features into a seven-dimensional footprint of gait-related artifacts, combining features of time, time-frequency, spatial, and source domains. EEG of N = 26 participants was recorded while standing and walking outdoors. Footprints of gait-related artifacts before and after two different artifact attenuation strategies (after artifact subspace reconstruction (ASR) and after subsequent independent component analysis [ICA]) were systematically different. We also evaluated topographies, morphologies, and signal-to-noise ratios (SNR) of button-press event-related potentials (ERP) before and after artifact handling, to confirm gait-artifact reduction specificity. Morphologies and SNR remained unchanged after artifact attenuation, whereas topographies improved in quality. Our results show that the footprint can provide a detailed assessment of gait-related artifacts and can be used to estimate the sensitivity of different artifact reduction strategies. Moreover, the analysis of button-press ERPs demonstrated its specificity, as processing did not only reduce gait-related artifacts but ERPs of interest remained largely unchanged. We conclude that the proposed footprint is well suited to characterize individual differences in gait-related artifact extent. In the future, it could be used to compare and optimize recording setups and processing pipelines comprehensively.
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Affiliation(s)
- Nadine Svenja Josée Jacobsen
- School of Medicine and Health Sciences, Department of Psychology, Neuropsychology Lab, University of Oldenburg, Oldenburg, Germany
| | - Sarah Blum
- School of Medicine and Health Sciences, Department of Psychology, Neuropsychology Lab, University of Oldenburg, Oldenburg, Germany
| | - Karsten Witt
- School of Medicine and Health Sciences, Department of Neurology and Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- School of Medicine and Health Sciences, Department of Psychology, Neuropsychology Lab, University of Oldenburg, Oldenburg, Germany
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22
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Saeedi M, Saeedi A, Maghsoudi A. Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals. Phys Eng Sci Med 2020; 43:1007-1018. [PMID: 32662038 DOI: 10.1007/s13246-020-00897-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/02/2020] [Indexed: 12/18/2022]
Abstract
The aim of this paper is to introduce a novel method using short-term EEG signals to separate depressed patients from healthy controls. Five common frequency bands (delta, theta, alpha, beta and gamma) were extracted from the signals as linear features, as well as, wavelet packet decomposition to break down signals into certain frequency bands. Afterwards, two entropy measures, namely sample entropy and approximate entropy were applied on the wavelet packet coefficients as nonlinear features, and significant features were selected via genetic algorithm (GA). Three machine-learning algorithms were used for classification; including support vector machine (SVM), multilayer perceptron (MLP) a novel enhanced K-nearest neighbors (E-KNN), which uses GA to optimize the feature-space distances and provides a feature importance index. The highest accuracy obtained by using frequency-based features was from gamma oscillations which resulted in 91.38%. Performance of nonlinear features were better compared to the frequency-based features and the results showed 94.28% accuracy. The combination of the features showed 98.44% accuracy with the new proposed E-KNN classifier.
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Affiliation(s)
- Maryam Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abdolkarim Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
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23
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Ortiz M, Iáñez E, Gaxiola-Tirado JA, Gutiérrez D, Azorín JM. Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation. Int J Neural Syst 2020; 30:2050038. [DOI: 10.1142/s0129065720500380] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphere.
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Affiliation(s)
- Mario Ortiz
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avenida Universidad sn. Ed. Innova, Elche, Alicante 03202, Spain
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Avenida Universidad sn. Ed. Innova, Elche, Alicante 03202, Spain
| | - Jorge A. Gaxiola-Tirado
- Center for Research and Advanced Studies (Cinvestav), Monterrey’s Unit, Vía del Conocimiento 201 PIIT, 66600, Apodaca NL 66600, Mexico
| | - David Gutiérrez
- Center for Research and Advanced Studies (Cinvestav), Monterrey’s Unit, Vía del Conocimiento 201 PIIT, 66600, Apodaca NL 66600, Mexico
| | - José M. Azorín
- Systems Engineering and Automation Department, Miguel Hernández University of Elche, Avenida Universidad sn. Ed. Innova, Elche, Alicante 03202, Spain
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A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level. Neural Netw 2020; 124:357-372. [DOI: 10.1016/j.neunet.2020.01.027] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 01/25/2023]
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25
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Design of a Network Permutation Entropy and Its Applications for Chaotic Time Series and EEG Signals. ENTROPY 2019. [PMCID: PMC7515378 DOI: 10.3390/e21090849] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Measuring the complexity of time series provides an important indicator for characteristic analysis of nonlinear systems. The permutation entropy (PE) is widely used, but it still needs to be modified. In this paper, the PE algorithm is improved by introducing the concept of the network, and the network PE (NPE) is proposed. The connections are established based on both the patterns and weights of the reconstructed vectors. The complexity of different chaotic systems is analyzed. As with the PE algorithm, the NPE algorithm-based analysis results are also reliable for chaotic systems. Finally, the NPE is applied to estimate the complexity of EEG signals of normal healthy persons and epileptic patients. It is shown that the normal healthy persons have the largest NPE values, while the EEG signals of epileptic patients are lower during both seizure-free intervals and seizure activity. Hence, NPE could be used as an alternative to PE for the nonlinear characteristics of chaotic systems and EEG signal-based physiological and biomedical analysis.
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