Improved recognition of error related potentials through the use of brain connectivity features.
Brain error processing plays a key role in goal-directed behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in brain-computer interface (BCI) system. We describe the first application of brain connectivity features for recognizing error-related signals in non-invasive BCI. EEG signal were recorded from 16 human subjects when they monitored stimuli moving in either correct or erroneous direction. Classification performance using waveform features, brain connectivity features and their combination were compared. The result of combined features yielded highest classification accuracy, 0:85. In addition, we also show that brain connectivity at theta band around 200 ms after stimuli carry highly discriminant information between error and correct trials. This paper provides evidence that the use of connectivity features improve the performance of an EEG based BCI.
Defitech Foundation Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland. huaijian.zhang@epfl.ch
Huaijian
H
Zhang
Ricardo
R
Chavarriaga
Mohit Kumar
MK
Goel
Lucian
L
Gheorghe
José del R
Jdel R
Millán
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors; D014584:User-Computer Interface