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Real time eye blink noise removal from EEG signals using morphological component analysis

Real time eye blink noise removal from EEG signals using morphological component analysis
Real time eye blink noise removal from EEG signals using morphological component analysis
This paper presents a method of removing the noise caused by eye blinks from an electroencephalogram (EEG) signal in real time based on morphological component analysis (MCA). This method sparsely represents both the eye blink and the EEG signal basis matrices using a Short Time Fourier Transform (STFT). This approach has two main advantages: 1) fast computation of the estimation of the signal coefficients using the basis pursuit algorithm 2) less memory requirement. The obtained result shows that the correlation coefficient between the raw EEG and the cleaned EEG is between 0.72 and 0.94 which implies that it is possible to remove eye blink noise from the EEG signal in real time without affecting an underlying brain signal.
Matiko, Joseph W.
21477818-ba95-4fd6-beb9-8f81a27a46a4
Beeby, Stephen
ba565001-2812-4300-89f1-fe5a437ecb0d
Tudor, John
90c9df33-7953-4d4f-9c74-0fe1d306911b
Matiko, Joseph W.
21477818-ba95-4fd6-beb9-8f81a27a46a4
Beeby, Stephen
ba565001-2812-4300-89f1-fe5a437ecb0d
Tudor, John
90c9df33-7953-4d4f-9c74-0fe1d306911b

Matiko, Joseph W., Beeby, Stephen and Tudor, John (2013) Real time eye blink noise removal from EEG signals using morphological component analysis. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Japan. 03 - 07 Jul 2013.

Record type: Conference or Workshop Item (Paper)

Abstract

This paper presents a method of removing the noise caused by eye blinks from an electroencephalogram (EEG) signal in real time based on morphological component analysis (MCA). This method sparsely represents both the eye blink and the EEG signal basis matrices using a Short Time Fourier Transform (STFT). This approach has two main advantages: 1) fast computation of the estimation of the signal coefficients using the basis pursuit algorithm 2) less memory requirement. The obtained result shows that the correlation coefficient between the raw EEG and the cleaned EEG is between 0.72 and 0.94 which implies that it is possible to remove eye blink noise from the EEG signal in real time without affecting an underlying brain signal.

Text Matiko.pdf - Accepted Manuscript
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More information

Published date: 4 June 2013
Venue - Dates: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Japan, 2013-07-03 - 2013-07-07
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 358670
URI: https://eprints.soton.ac.uk/id/eprint/358670
PURE UUID: 2592f4a9-8901-44de-8182-f55cb9ae8b22
ORCID for Stephen Beeby: ORCID iD orcid.org/0000-0002-0800-1759

Catalogue record

Date deposited: 17 Oct 2013 12:08
Last modified: 06 Jun 2018 13:07

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