Online Adaptive Brain–Computer Interface for Stroke Rehabilitation

• Main Authors: Wang, Yun-Chi, 王韻綺
• Other Authors: Chang, Tian-Sheuan
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/49516912218409784597

碩士 === 國立交通大學 === 電子工程學系 電子研究所 === 104 === The electroencephalographic (EEG) signals based brain-computer interface (BCI) system help stroke rehabilitation but face the signal nonstationary problem and results in lower effectiveness. To solve this problem, this thesis proposes an online adaptive BCI interface for stroke rehabilitation. The proposed approach adopts the full update of the feature extraction and classification from input data instead of the previous leaky update of either feature extraction or classification with old results and small amount of new input data. Our approach can improve accuracy up to 13% when compared to the previous method. This method enables significantly lower initial training time to 1.5 to 4 minutes for online adaptive BCI instead of 20 minutes in the previous approach. The final online adaptive BCI simulation can attain 81.77% accuracy in average for stroke patients with 24 trials window size and 20 second update rate, which is 6.7% better than that in non-adaptive online BCI.