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In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset,...
A problem of information separation in multichannel recordings is important in engineering applications such as brain computer/machine interfaces (BCI/BMI). Whereas this problem is not entirely new, engineering approaches connecting the mental states of humans and the observed electroencephalography (EEG) recordings are still in their infancy, mostly due to problems with electrophysiological denoising...
The common spatial patterns (CSP) algorithm has been widely used in EEG classification and brain computer interface (BCI). In this paper, we propose a multilinear formulation of the CSP, termed as TensorCSP or common tensor discriminant analysis (CTDA) for high-order tensor data. As a natural extension of CSP, the proposed algorithm uses the analogous optimization criteria in CSP and a new framework...
Saccade-related electroencephalogram (EEG) signals have been the subject of application oriented research by our group toward developing a brain computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. In signal processing method...
Electroencephalograms (EEGs) were recorded from eight Japanese speakers while they listened to Japanese and Spanish sentences (approximately 51 s each). The sentences were modulated in amplitude by a binary m-sequence and played forward or backward. A circular cross-correlation function was computed between the EEG signals and the m-sequence and averaged across subjects. Independent component analysis...
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