Single upper limb functional movements decoding from motor imagery EEG signals using wavelet neural network

Autor: Xiaobo Zhou, Xiayang Huang, Renling Zou
Rok vydání: 2021
Předmět:
Zdroj: Biomedical Signal Processing and Control. 70:102965
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2021.102965
Popis: In this study, we propose a wavelet neural network (WNN) to improve the performance of movements decoding from motor imagery (MI) EEG signals. Fifteen healthy subjects performed six functional movements of single upper limb: forearm pronation/supination, hand open/close, and elbow flexion/extension. The wavelet packet decomposition (WPD) was used to decompose MI EEG into sub-bands, and the statistical features were extracted from the sub-bands to code the functional movements. Subsequently, the principal component analysis (PCA) was used to select the best feature vector. The factors of mother wavelet, multidimensional wavelet function, number of channels, and the time of imaging segment were optimized through experiments. As a result, the best accuracy of 86.27 ± 6.98% was achieved with the optimized coif1 mother wavelet with a six-level decomposition, Mexican Hat wavelet as a hyperparameter, the time of imaging segment set as 0.7 s, and 61 channels data. In addition, to further verify the efficiency of the WNN classifier, the comparative experiments were conducted with the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and single hidden layer feedforward neural network (ANN). The results show that the accuracy of WNN is improved by about 15 ~ 40%, which proved the effectiveness of the WNN method. This study provides a new way to decode from motor imagery EEG in multi-movement classification.
Databáze: OpenAIRE
Externí odkaz: