Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Computer Science, Engineering, Brain-computer interface (BCI), multi-linear algebra, subspace learning, tensor, tensor regression, wearable electroencephalography (EEG), SELECTION, FILTERS, STADIUS-18-93, C16/15/059#53326574

Abstract:

© 2018 IEEE. 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.