Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

Maiko SAKAMOTO  Hiromi YAMAGUCHI  Toshimasa YAMAZAKI  Ken-ichi KAMIJO  Takahiro YAMANOI  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D    No.11    pp.1976-1981
Publication Date: 2015/11/01
Publicized: 2015/08/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7017
Type of Manuscript: PAPER
Category: Biocybernetics, Neurocomputing
BCI,  Bayesian network,  single-trial EEG,  ICA,  ECDL,  

Full Text: PDF(572.1KB)>>
Buy this Article

We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate between left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant difference between the two methods. Our BNM might reflect each subject's strategy for task execution.

open access publishing via