Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study.

Yue Li,Jianmin Zhang,Yile Jin,Marco Controzzi,Junming Zhu,Bangyu Cai,Shaomin Zhang,Xiaoxiang Zheng

doi:10.1155/2017/3435686

Abstract

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.

Highlights

Brain-machine interfaces (BMIs) have potential capabilities to bypass the interrupted motor pathways caused by neurological disorders or amputation and to build a direct communication between the brain and external devices by interpreting brain acitivities [1]
ECoG has been used to reconstruct high-dimensional arm movement [5, 6], predict movement directions [7] and single finger flexion [8,9,10,11], and classify hand gesture type [12, 13] as well as detect gross grasp movement [14, 15] with less training [16]. These studies demonstrate that the signal-tonoise ratio and the temporal-spatial resolution of the ECoG signals are sufficient to represent multiple hand gestures and provide commands to control a robotic hand in real time
We explored the feasibility of implementing an ECoG-based BMI which could decode real-time three hand gestures and control an artificial hand

Summary

Introduction

Brain-machine interfaces (BMIs) have potential capabilities to bypass the interrupted motor pathways caused by neurological disorders or amputation and to build a direct communication between the brain and external devices by interpreting brain acitivities [1]. ECoG has been used to reconstruct high-dimensional arm movement [5, 6], predict movement directions [7] and single finger flexion [8,9,10,11], and classify hand gesture type [12, 13] as well as detect gross grasp movement [14, 15] with less training [16] These studies demonstrate that the signal-tonoise ratio and the temporal-spatial resolution of the ECoG signals are sufficient to represent multiple hand gestures and provide commands to control a robotic hand in real time

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