Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays
Authors
Shivayogi V. Hiremath, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA.
Weidong Chen, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Qiushi Academy for Advanced Studies (QAAS), Zhejiang University Hangzhou, China.
Wei Wang, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Bioengineering, University of Pittsburgh Pittsburgh, PA, USA ; Clinical and Translational Science Institute, University of Pittsburgh Pittsburgh, PA, USA ; Center for the Neural Basis of Cognition, Carnegie Mellon University and the University of Pittsburgh Pittsburgh, PA, USA.
Stephen Foldes, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA ; Center for the Neural Basis of Cognition, Carnegie Mellon University and the University of Pittsburgh Pittsburgh, PA, USA.Follow
Ying Yang, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Center for the Neural Basis of Cognition, Carnegie Mellon University and the University of Pittsburgh Pittsburgh, PA, USA.
Elizabeth C. Tyler-Kabara, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Bioengineering, University of Pittsburgh Pittsburgh, PA, USA ; Department of Neurological Surgery, University of Pittsburgh Pittsburgh, PA, USA.
Jennifer L. Collinger, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA ; Department of Bioengineering, University of Pittsburgh Pittsburgh, PA, USA ; Center for the Neural Basis of Cognition, Carnegie Mellon University and the University of Pittsburgh Pittsburgh, PA, USA.
Michael L. Boninger, Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA ; Department of Bioengineering, University of Pittsburgh Pittsburgh, PA, USA ; Clinical and Translational Science Institute, University of Pittsburgh Pittsburgh, PA, USA.
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.
Keywords
BCI learning, BCI mapping, brain control, cognitive skill learning, human-computer interfaces, motor learning
Publication Date
1-1-2015
Publication Title
Frontiers in integrative neuroscience
Digital Object Identifier (DOI)
10.3389/fnint.2015.00040
Recommended Citation
Hiremath, Shivayogi V.; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C.; Collinger, Jennifer L.; and Boninger, Michael L., "Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays" (2015). Translational Neuroscience. 2195.
https://scholar.barrowneuro.org/neurobiology/2195
