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Brain Computer Interface (BCI) is mainly divided into two phases; calibration phase for training and feedback phase. A calibration phase is usually time-consuming, thereby, being likely to raise subjects’ fatigue at the early stage. For more convenient and applicable BCI system it should be investigated to reduce such preparation (calibration) time before feedback phase. Beamformer is a source imaging technique widely used in MEG/EEG source localization problem. It passes only signals produced at the designated source point and filters out other signals such as noise. We conjecture information in source space may be consistent over well trained and good subjects. This idea facilitates to reuse existing datasets from the same or different subjects. Using IVa data in BCI competition III, we constructed a classifier from other 4 subject’s training data and performance was evaluated in source domain. In this work, we observed the proposed approach worked well, resulting in relatively good accuracies (73.21%, 74.21%) for two subjects.

Calibration Time Reduction through Source Imaging in Brain Computer Interface (BCI)