Research on extraction and classification of EEG features for multi-class motor imagery

Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), W...

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Veröffentlicht in:2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) Jg. 1; S. 693 - 697
Hauptverfasser: Tang, Xuebin, Zhao, Jinchuang, Fu, Wenli
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2019
Schlagworte:
Brain modeling
brain-computer interface systems (BCI)
Classification algorithms
common spatial pattern (CSP)
Convolution
convolutional neural network (CNN)
Covariance matrices
Electroencephalography
Feature extraction
motor imagery EEG signals (MI-EEG)
Training
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Zusammenfassung:Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), We combine the one-versus-the-rest common spatial pattern (OVR-CSP) algorithm and a novel convolution neural networks (CNN) algorithm to extract features and classify 4-class MI-EEG signals. Firstly, the original EEG signal data is truncated and expanded by using a fixed-size overlapping window, the features of intercepted sub-signals are extracted by CSP algorithm and the obtained feature vectors are merged as the input sample matrix of CNN. Secondly, CNN algorithm performs second feature extraction and final classification on the input sample matrix. Finally, the validity of the proposed algorithm was verified by the datasets IIIa of the BCI2005 competition. The average recognition accuracy of the three subjects has reached 91.9%, this is an improvement over other algorithms in recent years.
DOI:10.1109/IAEAC47372.2019.8998049