title: Context-based lossless and near-lossless compression of EEG signals.
abstract: In this paper, we study compression techniques for electroencephalograph (EEG) signals. A variety of lossless compression techniques, including compress, gzip, bzip, shorten, and several predictive coding methods, are investigated and compared. The methods range from simple dictionary-based approaches to more sophisticated context modeling techniques. It is seen that compression ratios obtained by lossless compression are limited even with sophisticated context-based bias cancellation and activity-based conditional coding. Though lossy compression can yield significantly higher compression ratios while potentially preserving diagnostic accuracy, it is not usually employed due to legal concerns. Hence, we investigate a near-lossless compression technique that gives quantitative bounds on the errors introduced during compression. It is observed that such a technique gives significantly higher compression ratios (up to 3-bit/sample saving with less than 1% error). Compression results are reported for EEG's recorded under various clinical conditions.
authors.affiliation: Department of Computer Science, Polytechnic University, Brooklyn, NY 11201, USA.
authors.forename: N
authors.initials: N
authors.lastname: Memon
authors.forename: X
authors.initials: X
authors.lastname: Kong
authors.forename: J
authors.initials: J
authors.lastname: Cinkler
mesh_terms: D004569:Electroencephalography; D012815:Signal Processing, Computer-Assisted