A Time-Series Prediction Approach for Feature Extraction in a Brain-Computer Interface

Research output: Contribution to journalArticle

82 Citations (Scopus)
LanguageEnglish
Pages461-467
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Dec 2005

Cite this

@article{2a31f39e55c041b4848ccda41940b560,
title = "A Time-Series Prediction Approach for Feature Extraction in a Brain-Computer Interface",
author = "D Coyle and TM McGinnity and G Prasad",
note = "This paper presents a time-series prediction based signal processing concept for a brain-computer interface (BCI) to enhance signal separability, and improve accuracy and information transfer rate. The work is the basis of a PhD thesis awarded the IEEE Computational Intelligence Society Outstanding Dissertation Award 2008 and is now extensively used as the basis for ongoing research to develop an advanced BCI system. Through the paper, the authors have joined an EU FP7 proposal consortium, Advanced Multimodal Technology for Tetraplegic-patient Enablement in Communication and Control in the Home, involving Philips Research, Shadow Robot Company, and the National Rehabilitation Hospital, Ireland.",
year = "2005",
month = "12",
day = "1",
doi = "10.1109/TNSRE.2005.857690",
language = "English",
volume = "13",
pages = "461--467",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
number = "4",

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AU - Coyle, D

AU - McGinnity, TM

AU - Prasad, G

N1 - This paper presents a time-series prediction based signal processing concept for a brain-computer interface (BCI) to enhance signal separability, and improve accuracy and information transfer rate. The work is the basis of a PhD thesis awarded the IEEE Computational Intelligence Society Outstanding Dissertation Award 2008 and is now extensively used as the basis for ongoing research to develop an advanced BCI system. Through the paper, the authors have joined an EU FP7 proposal consortium, Advanced Multimodal Technology for Tetraplegic-patient Enablement in Communication and Control in the Home, involving Philips Research, Shadow Robot Company, and the National Rehabilitation Hospital, Ireland.

PY - 2005/12/1

Y1 - 2005/12/1

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DO - 10.1109/TNSRE.2005.857690

M3 - Article

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EP - 467

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

T2 - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

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