Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex

Chao Chen; Duk Shin; Hidenori Watanabe; Yasuhiko Nakanishi; Hiroyuki Kambara; Natsue Yoshimura; Atsushi Nambu; Tadashi Isa; Yukio Nishimura; Yasuharu Koike

doi:10.1016/j.neures.2014.03.010

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex

Neurosci Res. 2014 Jun:83:1-7. doi: 10.1016/j.neures.2014.03.010. Epub 2014 Apr 13.

Authors

Affiliations

¹ Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan.
² Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan. Electronic address: shinduk@cns.pi.titech.ac.jp.
³ Department of Developmental Physiology, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan.
⁴ Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan.
⁵ Department of Integrative Physiology, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan; Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan.
⁶ Department of Developmental Physiology, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan; Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan.
⁷ Department of Developmental Physiology, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Japan; Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan; Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Tokyo, Japan.
⁸ Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan; Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Japan.

PMID: 24726922
DOI: 10.1016/j.neures.2014.03.010

Abstract

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.

Keywords: Brain machine interfaces; Decoding force; Electrocorticography.

MeSH terms

Algorithms*
Animals
Brain-Computer Interfaces
Electroencephalography / methods
Female
Hand Strength / physiology*
Haplorhini
Linear Models
Male
Sensorimotor Cortex / physiology*
Signal Processing, Computer-Assisted*