TY - GEN
T1 - EEG Feature Fusion for Person Identification Using Efficient Machine Learning Approach
AU - Alyasseri, Zaid Abdi Alkareem
AU - Al-Betar, Mohammed Azmi
AU - Awadallah, Mohammed A.
AU - Makhadmeh, Sharif Naser
AU - Alomari, Osama Ahmad
AU - Abasi, Ammar Kamal
AU - Doush, Iyad Abu
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. This paper proposed a new method for EEG feature extraction based on fusing different EEG features. In general, EEG feature extraction can be categorized into three types which are time domain, frequency domain, and time-frequency domain features. This paper also applied several supervised learning approaches to select the efficient classifier for EEG-based person identification. The performance of the proposed method is tested using standard EEG datasets, namely, EEG Motor Movement/Imagery Dataset. The results are evaluated using four common criteria which are: accuracy rate (ACCEEC), sensitivity (SenEEC), specificity (SpeEEC) and F-score (FSEEC). The experiment results show that the fusion approach achieves better results compared with a traditional EEG feature extraction approach. The proposed fusion feature method is recommended to apply in more challenging signal problem instances, such as user authentication or early detection of epilepsy based on EEG signals.
AB - Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. This paper proposed a new method for EEG feature extraction based on fusing different EEG features. In general, EEG feature extraction can be categorized into three types which are time domain, frequency domain, and time-frequency domain features. This paper also applied several supervised learning approaches to select the efficient classifier for EEG-based person identification. The performance of the proposed method is tested using standard EEG datasets, namely, EEG Motor Movement/Imagery Dataset. The results are evaluated using four common criteria which are: accuracy rate (ACCEEC), sensitivity (SenEEC), specificity (SpeEEC) and F-score (FSEEC). The experiment results show that the fusion approach achieves better results compared with a traditional EEG feature extraction approach. The proposed fusion feature method is recommended to apply in more challenging signal problem instances, such as user authentication or early detection of epilepsy based on EEG signals.
KW - EEG
KW - EEG Identification
KW - Feature Extraction
KW - Feature Fusion
KW - SVM-RBF
KW - Time-Frequency domain
UR - https://www.scopus.com/pages/publications/85124002345
U2 - 10.1109/PICICT53635.2021.00029
DO - 10.1109/PICICT53635.2021.00029
M3 - Conference contribution
AN - SCOPUS:85124002345
T3 - Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
SP - 97
EP - 102
BT - Proceedings - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021
Y2 - 28 September 2021 through 29 September 2021
ER -