Paper Info
Title
A Visual-Haptic Neurofeedback Training Improves Sensorimotor Cortical Activations And Bci Performance
Abstract
Neurofeedback training (NFT) could provide a novel way to investigate or restore the impaired brain function and neuroplasticity. However, it remains unclear how much the different feedback modes can contribute to NFT training. Specifically, whether they can enhance the cortical activations for motor training? To this end, our study proposed a brain-computer interface (BCI) based visual-haptic NFT incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10Hz, alpha_2: 11-13Hz, beta_1: 15-20Hz and beta_2: 22-28Hz) lateralized relative event-related desynchronization (IrERD) patterns were significantly enhanced after NFT. And the classification performance was also significantly improved, achieving a similar to 9% improvement and reaching similar to 85% in mean classification accuracy from a relatively low MI-BCI performance. These findings validate the feasibility of our proposed visual- haptic NFT approach to improve sensorimotor cortical activations and BCI performance during motor training.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8856389
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Neuroscience,Computer science,Brain–computer interface,Artificial intelligence,Proprioception,Neuroplasticity,Haptic technology,Neurofeedback
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Zhongpeng Wang104.06
Yijie Zhou202.37
Long Chen303.04
Bin Gu4101988.98
Shuang Liu53622.95
Minpeng Xu62717.17
Hongzhi Qi74920.61
Feng He8169.45
Dong Ming910551.47