Using NIRS as a predictor for EEG-based BCI performance.
Multimodal recordings of EEG and NIRS of 14 subjects are analyzed in the context of sensory-motor based Brain Computer Interface (BCI). Our findings indicate that performance fluctuations of EEG-based BCI control can be predicted by preceding Near-Infrared Spectroscopy (NIRS) activity. These NIRS-based predictions are then employed to generate new, more robust EEG-based BCI classifiers, which enhance classification significantly, while at the same time minimize performance fluctuations and thus increase the general stability of BCI performance.
Department of Computer Science, Technical University Berlin, Germany. fazli@cs.tu-berlin.de
Siamac
S
Fazli
Jan
J
Mehnert
Jens
J
Steinbrink
Benjamin
B
Blankertz
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D056228:Feedback, Sensory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010101:Oxygen Consumption / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D055815:Young Adult