By Topic

Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Pengfei Wei ; Shenzhen Key Lab of Neuropsychiatric Modulation, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China ; Wei He ; Yi Zhou ; Liping Wang

Voluntarily modulating neural activity plays a key role in brain-computer interface (BCI). In general, the self-regulated neural activation patterns are used in the current BCI systems involving the repetitive trainings with feedback for an attempt to achieve a high-quality control performance. With the limitation posed by the training procedure in most BCI studies, the present work aims to investigate whether directly modulating the neural activity by using an external method could facilitate the BCI control. We designed an experimental paradigm that combines anodal transcranial direct current stimulation (tDCS) with a motor imagery (MI)-based feedback EEG BCI system. Thirty-two young and healthy human subjects were randomly assigned to the real and sham stimulation groups to evaluate the effect of tDCS-induced EEG pattern changes on BCI classification accuracy. Results showed that the anodal tDCS obviously induces sensorimotor rhythm (SMR)-related event-related desynchronization (ERD) pattern changes in the upper-mu (10-14 Hz) and beta (14-26 Hz) rhythm components. Both the online and offline BCI classification results demonstrate that the enhancing ERD patterns could conditionally improve BCI performance. This pilot study suggests that the tDCS is a promising method to help the users to develop reliable BCI control strategy in a relatively short time.

Published in:

IEEE Transactions on Neural Systems and Rehabilitation Engineering  (Volume:21 ,  Issue: 3 )