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Front Integr Neurosci. 2015 Jun 10;9:40. doi: 10.3389/fnint.2015.00040. eCollection 2015.

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

Author information

1
Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA.
2
Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Qiushi Academy for Advanced Studies (QAAS), Zhejiang University Hangzhou, China.
3
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.
4
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.
5
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.
6
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.
7
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.
8
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

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