SINGLE-CHANNEL EEG CLASSIFICATION BY MULTI-CHANNEL TENSOR SUBSPACE LEARNING AND REGRESSION

The classification of brain states using neural recordings such as electroencephalography (EEG) finds applications in both medical and non-medical contexts, such as detecting epileptic seizures or discriminating mental states in brain-computer interfaces, respectively. Although this endeavor is well-established, existing solutions are typically restricted to lab or hospital conditions because they operate on recordings from a set of EEG electrodes that covers the whole head. By contrast, a true breakthrough for these applications would be the deployment `in the real world', by means of wearable devices that encompass just one (or a few) channels. Such a reduction of the available information inevitably makes the classification task more challenging. We tackle this issue by means of a multilinear subspace learning step (using data from multiple channels during training) and subsequently solving a regression problem with a low-rank structure to classify new trials (using data from only a single channel during testing). We demonstrate the feasibility of this approach on EEG data recorded during a mental arithmetic task.