Common Spatial Pattern Patches: online evaluation on BCI-naive users.
Brain-Computer Interfaces (BCI) based on the voluntary modulation of sensorimotor rhythms (SMRs) induced by motor imagery are very prominent because allow a continuous control of the external device. Nevertheless, the design of a SMR-based BCI system that provides every user with a reliable BCI control from the first session, i.e., without extensive training, is still a big challenge. Considerable advances in this direction have been made by the machine learning co-adaptive calibration approach, which combines online adaptation techniques with subject learning in order to offer the user a feedback from the beginning of the experiment. Recently, based on offline analyses, we proposed the novel Common Spatial Patterns Patches (CSPP) technique as a good candidate to improve the co-adaptive calibration. CSPP is an ensemble of localized spatial filters, each of them optimized on subject-specific data by CSP analysis. Here, the evaluation of CSPP in online operation is presented for the first time. Results on three BCI-naive participants show indeed promising results. All three users reach the threshold criterion of 70% accuracy within one session, even one candidate for whom the weak SMR at rest predicted deficient BCI control. Concurrent recordings of the SMR during a relax condition as well as the course of BCI performance indicate a clear learning effect.
Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany. claudia.sannelli@tu-berlin.de
Claudia
C
Sannelli
Carmen
C
Vidaurre
Klaus-Robert
KR
Müller
Benjamin
B
Blankertz
D000465:Algorithms; D001676:Biofeedback, Psychology / Q000379:methods / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*