TitleDecoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space.
Publication TypeJournal Article
Year of Publication2018
AuthorsXygonakis, Ioannis, Athanasiou Alkinoos, Pandria Niki, Kugiumtzis Dimitris, and Bamidis Panagiotis
JournalComput Intell Neurosci
Date Published2018
KeywordsAlgorithms, Brain, Brain-Computer Interfaces, Electroencephalography, Humans, Imagination, Motor Activity, Signal Processing, Computer-Assisted

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

Alternate JournalComput Intell Neurosci
PubMed ID30154834
PubMed Central IDPMC6092991