Paper Info
Title
Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis
Abstract
We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroen-cephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.
Year
DOI
Venue
2007
10.1155/2007/41468
Comp. Int. and Neurosc.
Field
DocType
Volume
Computer science,Brain–computer interface,Interfacing,Brain activity and meditation,Artificial intelligence,Electroencephalography,Spatial filter,Pattern recognition,Speech recognition,Independent component analysis,Rhythm,Machine learning,Motor imagery
Journal
2007,
Citations 
PageRank 
References 
12
0.74
12
Authors
2
Name
Order
Citations
PageRank
Suogang Wang1121.42
Christopher J. James220621.93