Featureless EMG pattern recognition based on convolutional neural network

Too Jing Wei (Universiti Teknikal Malaysia Melaka)
Abdul Rahim Bin Abdullah (Universiti Teknikal Malaysia Melaka)
Norhashimah Binti Mohd Saad (Universiti Teknikal Malaysia Melaka)
Nursabillilah Binti Mohd Ali (Universiti Teknikal Malaysia Melaka)
Tengku Nor Shuhada Binti Tengku Zawawi (Universiti Teknikal Malaysia Melaka)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 14, No 3: June 2019

In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.