Compressive sensing scalp EEG signals: implementations and practical performance.
Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is...
| Published in: | Medical & Biological Engineering & Computing Vol. 50; no. 11; pp. 1137 - 1146 |
|---|---|
| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Nov2012
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104067348&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104067348 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2012 vid: 50 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104067348 NLM21947867 2011762760 10.1007/s11517-011-0832-1 NLM21947867 104067348 ppf: 1137 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Compressive sensing scalp EEG signals: implementations and practical performance. aug: au: Abdulghani AM Casson AJ Rodriguez-Villegas E Abdulghani, Amir M Casson, Alexander J Rodriguez-Villegas, Esther affil: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK sug: subj: Image Processing, Computer Assisted Methods Electroencephalography Methods Signal Processing, Computer Assisted Equipment and Supplies Computers and Computerization Equipment Design Human Miniaturization Scalp Sensitivity and Specificity ab: Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance 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. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|