A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury

Abstract

This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (> 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.

Fig. 1

Recording of 57-channel surface EMG from forearm and hand muscles of an SCI subject. The bottom figure shows the stretchable strap used for the recording on which 8 surface electrodes are evenly distributed.

Fig. 2

Illustrations of six different hand grasp patterns used in this study.

Fig. 3

The effect of the number of feature dimensions on the classification performance averaged cross all the subjects using ULDA and PCA respectively. The TD feature set and the LDA classifier were used in this example.

Fig. 4

Class-to-class confusion matrices derived from Subject 1 and Subject 2, with and without application of majority vote (MV) for classification, respectively. Results are averaged as percentages. The results along the main diagonal, shaded in black, are correct classifications (accuracy) and those off the main diagonal, shaded in grey, are incorrect classifications (error rate). The TD feature set and LDA classifier were used in this example.

Fig. 5

Box plots of the overall classification accuracies from 9 subjects with different combinations of feature sets and classifiers

Fig. 6

The classification performance with limited number of EMG channels selected using the SFS method. The LDA classifier was used with different feature sets in this example.