Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces
(2014)
Presentation / Conference
Haider, R., Prasad, G., Li, Y., & Cecotti, H. (2014, November). Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces. Presented at Institute of Electrical and Electronics Engineers (IEEE) International Conference on Bioinformatics and Biomedicine, Belfast, UK
A major challenge to devising robust brain-computer interfaces (BCIs) based on electroencephalogram (EEG) data is the immanent non-stationary characteristics of EEG signals. Statistical properties of the signals may shift during inter-or-intra sessio... Read More about Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces.