Single-Trial EEG Source Reconstruction for Brain-Computer Interface

• Published in: IEEE transactions on biomedical engineering Vol. 55; no. 5; pp. 1592 - 1601
• Main Authors: Noirhomme, Quentin, Kitney, Richard I., Macq, Benoit
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bit rate; Brain; Brain - physiology; Brain Mapping - methods; Brain-computer interface (BCI); brain-machine interface; classification; Electrodes; Electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Engineering, computing & technology; Evoked Potentials - physiology; Humans; Image reconstruction; Ingénierie, informatique & technologie; inverse problem; Man-Machine Systems; Methods; Neurons; Pattern Recognition, Automated - methods; Reconstruction algorithms; Remote sensing; source reconstruction; Spatial resolution; Usability; User-Computer Interface; inverse problem; classifcation; Electroencephalogram; EEG; brain-computer interface; source reconstruction; BCI; brain-machine interface
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2007.913986

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.