Source Space Based Brain Computer Interface

Abstract

Non-invasive Brain Computer Interface (BCI) using EEG or MEG is one of hot research areas nowadays. This technology gives a new way to communicate between human and computer or machine. BCI system enables persons to command a computer without spontaneous limb movement by measuring the brain signal and interpreting the intention of users in real-time basis. MEG/EEG signal at sensor should be combination of signals originated from infinitely many sources. So, signals at sensor are so noisy and are hard to discriminate different state information (feature), which contains implicitly user’s intention. In general, it is too difficult to improve the performance of EEG based BCI without seeking better feature extraction. For seeking different features from the existing features, we try to do source localization in the belief that projection of EEG data into source space enables us to see unseen information in sensor space. Eigenbased minimum variance beamformer is introduced for this purpose in this work. It gives reconstructed source image on each voxel and is too robust to noise as well as fast (real-time reconstruction is tractable in some sense), which is well-suited to BCI system. Each trial for the given condition is projected into source space by beamformer localizer. Then in the source space feature extractions and classification are attempted.

In this work, left-right hand motor imagery experimental paradigm was used, and common spatial pattern (CSP) and Fisher’s linear discriminant analysis (FLDA) were applied for feature extraction and classification, respectively. In the specific regions of interest (for example, regions under the C3 and C4), voxel-based CSP is applied and compared with sensor-based CSP. Their BCI performance are investigated and compared for various datasets.