Limits...
Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis.

Wang S, James CJ - Comput Intell Neurosci (2007)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Signal Processing and Control Group, ISVR, University of Southampton, Southampton SO17 1BJ, UK. sgw@soton.ac.uk

ABSTRACT
We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (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.

No MeSH data available.


Related in: MedlinePlus

The power feature outputs for Subject “ay” ontesting set. (a) shows the power features on C3 using unprocessed data; (b)shows the power features on C3 after cICA processing. A circle denotes thepower feature for Target A (right hand imagination); a star indicates the powerfeature for Target B (right foot imagination).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2266977&req=5

fig7: The power feature outputs for Subject “ay” ontesting set. (a) shows the power features on C3 using unprocessed data; (b)shows the power features on C3 after cICA processing. A circle denotes thepower feature for Target A (right hand imagination); a star indicates the powerfeature for Target B (right foot imagination).


Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis.

Wang S, James CJ - Comput Intell Neurosci (2007)

The power feature outputs for Subject “ay” ontesting set. (a) shows the power features on C3 using unprocessed data; (b)shows the power features on C3 after cICA processing. A circle denotes thepower feature for Target A (right hand imagination); a star indicates the powerfeature for Target B (right foot imagination).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266977&req=5

fig7: The power feature outputs for Subject “ay” ontesting set. (a) shows the power features on C3 using unprocessed data; (b)shows the power features on C3 after cICA processing. A circle denotes thepower feature for Target A (right hand imagination); a star indicates the powerfeature for Target B (right foot imagination).
Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Signal Processing and Control Group, ISVR, University of Southampton, Southampton SO17 1BJ, UK. sgw@soton.ac.uk

ABSTRACT
We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (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.

No MeSH data available.


Related in: MedlinePlus