Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays

Shivayogi V. Hiremath, Weidong Chen, Wei Wang, Stephen Foldes, Ying Yang, Elizabeth C. Tyler-Kabara, Jennifer L. Collinger, Michael L. Boninger

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Original languageEnglish
Article number40
Pages (from-to)1-10
Number of pages10
JournalFrontiers in Integrative Neuroscience
Volume9
Issue numberJune
DOIs
StatePublished - Jun 10 2015

Keywords

  • BCI learning
  • BCI mapping
  • Brain control
  • Cognitive skill learning
  • Human-computer interfaces
  • Motor learning

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