Translation of EEG spatial filters from resting to motor imagery using independent component analysis.
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters.
Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, San Diego, California, United States of America. yijun@sccn.ucsd.edu
Yijun
Y
Wang
Yu-Te
YT
Wang
Tzyy-Ping
TP
Jung
D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005240:Feasibility Studies; D006225:Hand / Q000502:physiology; D006801:Humans; D008606:Mental Processes; D009068:Movement / Q000502:physiology*; D012146:Rest / Q000523:psychology*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013223:Statistics as Topic