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

Research output: Contribution to journalArticle

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalNeuroscience Research
Volume83
DOIs
Publication statusPublished - 2014 Jun 1

Keywords

  • Brain machine interfaces
  • Decoding force
  • Electrocorticography

ASJC Scopus subject areas

  • Neuroscience(all)

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    Chen, C., Shin, D., Watanabe, H., Nakanishi, Y., Kambara, H., Yoshimura, N., Nambu, A., Isa, T., Nishimura, Y., & Koike, Y. (2014). Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex. Neuroscience Research, 83, 1-7. https://doi.org/10.1016/j.neures.2014.03.010