+ Site Statistics
References:
52,654,530
Abstracts:
29,560,856
PMIDs:
28,072,755
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn

+ Translate
+ Recently Requested

Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system



Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system



Journal of Neuroengineering and Rehabilitation 4: 11



Recently, successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. For a multi-channel BI system, however, the high dimensionality of the generated wavelet features space poses a challenging problem. In this paper, a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel, self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features, resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to further reduce its dimensionality and select the best set of features. An offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another. The proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users.

(PDF emailed within 0-6 h: $19.90)

Accession: 051607313

Download citation: RISBibTeXText

PMID: 17470288

DOI: 10.1186/1743-0003-4-11



Related references

Adaptive classification in a self-paced hybrid brain-computer interface system. Conference Proceedings 2012: 3274-3279, 2013

Automatic artefact removal in a self-paced hybrid brain- computer interface system. Journal of Neuroengineering and Rehabilitation 9: 50, 2013

A self produced mother wavelet feature extraction method for motor imagery brain-computer interface. Conference Proceedings 2013: 4302-4305, 2015

A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery. Journal of Neuroscience Methods 244: 26-32, 2015

Effect of eye-blinks on a self-paced brain interface design. Clinical Neurophysiology 118(7): 1639-1647, 2007

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control. IEEE Transactions on Biomedical Circuits and Systems 11(4): 729-742, 2016

Recent studies in the design of a self-paced brain interface with low false positive rate. Conference Proceedings 1: 2944-2949, 2007

A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms. Journal of Computational Neuroscience 23(1): 21-37, 2007

Self-Paced Operation of a Wheelchair Based on a Hybrid Brain-Computer Interface Combining Motor Imagery and P300 Potential. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(12): 2516-2526, 2018

EEG-fTCD Hybrid brain-computer interface using template matching and wavelet decomposition. Journal of Neural Engineering 2019, 2019

A self-paced brain-computer interface system with a low false positive rate. Journal of Neural Engineering 5(1): 9-23, 2008

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomedical Engineering Online 16(1): 5, 2017

Automatic user customization for improving the performance of a self-paced brain interface system. Medical & Biological Engineering & Computing 44(12): 1093-1104, 2006

Swarm intelligence based wavelet coefficient feature selection for mass spectral classification: an application to proteomics data. Analytica Chimica Acta 651(1): 15-23, 2009

On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation. International Journal of Remote Sensing 27(1/2): 73-104, 2006