A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition

• Published in: Biomedical signal processing and control Vol. 68; p. 102587
• Main Authors: Wei, Pengna, Zhang, Jinhua, Tian, Feifei, Hong, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021
• Subjects: Electroencephalogram (EEG); Feature dimension; Feature-classifier combination; Gait phases recognition; Surface electromyography (sEMG); Surface electromyography (sEMG); Electroencephalogram (EEG); Gait phases recognition; Feature-classifier combination; Feature dimension
• ISSN: 1746-8094; 1746-8108; 1746-8108
• DOI: 10.1016/j.bspc.2021.102587

•Various dimensions of features and different classifiers applied for gait phases recognition.•The wider value distribution of features, the better accuracy of gait recognition.•Two-dimensional feature sets with KNN are suitable for online gait recognition.•Thirty-seven-dimensional feature sets achieved the highest classification accuracy. Surface electromyography (sEMG) and electroencephalogram (EEG) can be utilized to discriminate gait phases. However, the classification performance of various combination methods of the features extracted from sEMG and EEG channels for seven gait phase recognition has yet to be discussed. This study investigates the effectiveness of various dimensions of feature sets with different neural network algorithms in multiclass discrimination of gait phases. There are thirty-seven feature sets (slope sign change (SSC) of eight sEMG and twenty-one EEG channels, mean absolute value (MAV) of eight sEMG channels) and three classifiers (Linear Discriminant Analysis (LDA), K-nearest neighbor (KNN), Kernel Support Vector Machine (KSVM)) were utilized. The thirty-seven one-dimensional and six two-dimensional feature sets were applied to LDA and KNN, twenty-one-dimensional and thirty-seven-dimensional feature sets were applied to three optimized KSVM for gait phase recognition. We found that thirty-seven-dimensional feature sets with grid search KSVM achieved the highest classification accuracy (98.56 ± 1.34 %) and the time consumption was 26.37 s. The average time consumption of two-dimensional feature sets with KNN was the shortest (0.33 s). The SSC of sEMG with wider values distributions than others obtained a high performance. This indicates the wider the value distribution of features, the better accuracy of gait recognition. The findings suggest that a multi-dimensional feature set composed of EEG and sEMG features with KSVM achieved good performance. Considering execution time and recognition rate, two-dimensional feature sets with KNN are suitable for online gait recognition, thirty-seven-dimensional feature sets with KSVM are more likely to be used for off-line gait analysis.