A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

D Coyle; G Prasad; Martin McGinnity

doi:10.1155/ASP.2005.3141

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

D Coyle, G Prasad, Martin McGinnity

Research output: Contribution to journal › Article › peer-review

57 Citations (SciVal)

Original language	English
Pages (from-to)	3141-3151
Number of pages	11
Journal	EURASIP Journal on Advances in Signal Processing
Volume	2005
Issue number	19
DOIs	https://doi.org/10.1155/ASP.2005.3141
Publication status	Published - 1 Nov 2005

Bibliographical note

This paper presents a comprehensive overview of issues related to brain-computer interface (BCI) development and methods of extracting time-localised frequency spectral information from electroencephalogram signals to improve overall performance. This special issue journal publication included articles from many of the most prominent BCI research groups. This paper has been cited in two recent comprehensive surveys of BCI designs (Mason, Annals of Biomedical Engineering, 2007; Bashashati, Neural Engineering, 2007); in Mason (2007) the authors are identified as prominent researchers in the field. This work is part of a PhD thesis awarded the IEEE Computational Intelligence Society Outstanding Dissertation Award 2008.

Access to Document

10.1155/ASP.2005.3141

Cite this

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title = "A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures",

author = "D Coyle and G Prasad and Martin McGinnity",

note = "This paper presents a comprehensive overview of issues related to brain-computer interface (BCI) development and methods of extracting time-localised frequency spectral information from electroencephalogram signals to improve overall performance. This special issue journal publication included articles from many of the most prominent BCI research groups. This paper has been cited in two recent comprehensive surveys of BCI designs (Mason, Annals of Biomedical Engineering, 2007; Bashashati, Neural Engineering, 2007); in Mason (2007) the authors are identified as prominent researchers in the field. This work is part of a PhD thesis awarded the IEEE Computational Intelligence Society Outstanding Dissertation Award 2008.",

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