Impact of lossy data compression techniques on EEG-based pattern recognition systems

The Binh NGUYEN, Wanli MA, Dat TRAN

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

4 Citations (Scopus)

Abstract

Electroencephalogram (EEG) data compression has been used to reduce the space for storage and speed up the data circulation. Albeit lossy compression techniques achieve a much higher compression ratio than lossless ones, they introduce the loss of information in reconstructed data, which may affect to the performance of EEG-based pattern recognition systems. In this paper, we investigate the impact of lossy compression techniques on the performance of EEG-based pattern recognition systems including seizure recognition and person recognition. Our experiments are conducted on two public databases using two different EEG lossy compression techniques. Experimental results show that the recognition performance is not significantly reduced when using lossy techniques at high compression ratios.
Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
Place of PublicationBeijing, PRC
PublisherIEEE
Pages2308-2313
Number of pages6
ISBN (Electronic)9781538637883
ISBN (Print)9781538637890
DOIs
Publication statusPublished - 26 Nov 2018
Event24th International Conference on Pattern Recognition 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018
http://www.icpr2018.net/

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition 2018
Abbreviated titleICPR
Country/TerritoryChina
CityBeijing
Period20/08/1824/08/18
Internet address

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