TY - GEN
T1 - EEG Classification-based Comparison Study of Motor-Imagery Brain-Computer Interface
AU - Djelloul, Kheira
AU - Belkacem, Abdelkader Nasreddine
N1 - Funding Information:
AB acknowledges support from the United Arab Emirates University (Grant number: G00003270 “31T130”).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.
AB - For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.
KW - Brain computer interface
KW - EEG signal classification
KW - Electroencephalogram (EEG)
KW - KNN
KW - LDA
KW - Motor imagery BCI
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85125964751&partnerID=8YFLogxK
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U2 - 10.1109/ICRAMI52622.2021.9585902
DO - 10.1109/ICRAMI52622.2021.9585902
M3 - Conference contribution
AN - SCOPUS:85125964751
T3 - Proceedings - 2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021
BT - Proceedings - 2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021
A2 - Bendjenna, Hakim
A2 - Meraoumia, Abdallah
A2 - Amroune, Mohamed
A2 - Ridda, Laouar Med
A2 - Boumaza, Nouri
A2 - Abdelmalek, Salem
A2 - Ouannas, Adel
A2 - Laimeche, Lakhdar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Recent Advances in Mathematics and Informatics, ICRAMI 2021
Y2 - 21 September 2021 through 22 September 2021
ER -