EEG electrode selection for person identification thru a genetic-algorithm method.
New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
|---|---|
| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138200097&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200097 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200097 138200097 138200097 10.1007/s10916-019-1364-8 138200097 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: EEG electrode selection for person identification thru a genetic-algorithm method. aug: au: Albasri, Ahmed Abdali-Mohammadi, Fardin Fathi, Abdolhossein affil: Department of Computer and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran sug: subj: Electroencephalography Electrodes Genetic Algorithms Biometrics Human Descriptive Statistics Signal Processing, Computer Assisted Image Processing, Computer Assisted ab: New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor. New methods and algorithms have attempted to reduce EEG processing time, including a reduction of the number of electrodes and segmenting the EEG data into its typical frequency bands. This work complements other efforts by proposing a genetic algorithm to reduce the number of necessary electrodes for measurements by EEG devices. Using a public EEG dataset of 109 subjects who underwent relaxation with eye-open and eye-closed stimuli, we aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli. The results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes. Moreover, higher frequency bands required a fewer number of electrodes for identification compared with lower frequency bands. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|