Quantifying the performance of compressive sensing on scalp EEG signals

Amir M. Abdulghani, Alexander J. Casson, Esther Rodriguez-Villegas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

Abstract

Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data.

Original languageEnglish
Title of host publication2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
DOIs
Publication statusPublished - 2010
Event2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 - Roma, Italy
Duration: Nov 7 2010Nov 10 2010

Other

Other2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
CountryItaly
CityRoma
Period11/7/1011/10/10

Fingerprint

Electroencephalography
Scalp
Data Compression
Brain-Computer Interfaces
Brain computer interface
Data compression
Nervous System Diseases
Magnetic resonance imaging
Communication systems

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Abdulghani, A. M., Casson, A. J., & Rodriguez-Villegas, E. (2010). Quantifying the performance of compressive sensing on scalp EEG signals. In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 [5702814] https://doi.org/10.1109/ISABEL.2010.5702814

Quantifying the performance of compressive sensing on scalp EEG signals. / Abdulghani, Amir M.; Casson, Alexander J.; Rodriguez-Villegas, Esther.

2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010. 2010. 5702814.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdulghani, AM, Casson, AJ & Rodriguez-Villegas, E 2010, Quantifying the performance of compressive sensing on scalp EEG signals. in 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010., 5702814, 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010, Roma, Italy, 11/7/10. https://doi.org/10.1109/ISABEL.2010.5702814
Abdulghani AM, Casson AJ, Rodriguez-Villegas E. Quantifying the performance of compressive sensing on scalp EEG signals. In 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010. 2010. 5702814 https://doi.org/10.1109/ISABEL.2010.5702814
Abdulghani, Amir M. ; Casson, Alexander J. ; Rodriguez-Villegas, Esther. / Quantifying the performance of compressive sensing on scalp EEG signals. 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010. 2010.
@inproceedings{dd58b931174149619a6d1743c3c4eda1,
title = "Quantifying the performance of compressive sensing on scalp EEG signals",
abstract = "Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data.",
author = "Abdulghani, {Amir M.} and Casson, {Alexander J.} and Esther Rodriguez-Villegas",
year = "2010",
doi = "10.1109/ISABEL.2010.5702814",
language = "English",
isbn = "9781424481323",
booktitle = "2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010",

}

TY - GEN

T1 - Quantifying the performance of compressive sensing on scalp EEG signals

AU - Abdulghani, Amir M.

AU - Casson, Alexander J.

AU - Rodriguez-Villegas, Esther

PY - 2010

Y1 - 2010

N2 - Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data.

AB - Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data.

UR - http://www.scopus.com/inward/record.url?scp=79952010197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952010197&partnerID=8YFLogxK

U2 - 10.1109/ISABEL.2010.5702814

DO - 10.1109/ISABEL.2010.5702814

M3 - Conference contribution

AN - SCOPUS:79952010197

SN - 9781424481323

BT - 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010

ER -