Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays
Genre
Journal ArticleDate
2015-06-10Author
Hiremath, SVChen, W
Wang, W
Foldes, S
Yang, Y
Tyler-Kabara, EC
Collinger, JL
Boninger, ML
Subject
BCI learningBCI mapping
brain control
cognitive skill learning
human-computer interfaces
motor learning
Permanent link to this record
http://hdl.handle.net/20.500.12613/5223
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Show full item recordDOI
10.3389/fnint.2015.00040Abstract
© 2015 Hiremath, Chen, Wang, Foldes, Yang, Tyler-Kabara, Collinger and Boninger. 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.Citation to related work
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http://dx.doi.org/10.34944/dspace/5205