PMID- 17299228
OWN - NLM
STAT- MEDLINE
DCOM- 20070424
LR  - 20191210
IS  - 0162-8828 (Print)
IS  - 0098-5589 (Linking)
VI  - 29
IP  - 4
DP  - 2007 Apr
TI  - Biometrics from brain electrical activity: a machine learning approach.
PG  - 738-42
AB  - The potential of brain electrical activity generated as a response to a visual 
      stimulus is examined in the context of the identification of individuals. 
      Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics 
      is established, whereby energy features of the gamma band within VEP signals were 
      of particular interest. A rigorous analysis is conducted which unifies and 
      extends results from our previous studies, in particular, with respect to 1) 
      increased bandwidth, 2) spatial averaging, 3) more robust power spectrum 
      features, and 4) improved classification accuracy. Simulation results on a large 
      group of subject support the analysis.
FAU - Palaniappan, Ramaswamy
AU  - Palaniappan R
AD  - Department of Computer Science, University of Essex, Colchester, Wivenhoe Park, 
      UK. rpalan@essex.ac.uk
FAU - Mandic, Danilo P
AU  - Mandic DP
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PL  - United States
TA  - IEEE Trans Pattern Anal Mach Intell
JT  - IEEE transactions on pattern analysis and machine intelligence
JID - 9885960
SB  - IM
MH  - Algorithms
MH  - *Artificial Intelligence
MH  - Biometry/*methods
MH  - Brain/*physiology
MH  - Brain Mapping/*methods
MH  - Electroencephalography/*methods
MH  - Evoked Potentials/*physiology
MH  - Humans
MH  - Pattern Recognition, Automated/*methods
EDAT- 2007/02/15 09:00
MHDA- 2007/04/25 09:00
CRDT- 2007/02/15 09:00
PHST- 2007/02/15 09:00 [pubmed]
PHST- 2007/04/25 09:00 [medline]
PHST- 2007/02/15 09:00 [entrez]
AID - 10.1109/TPAMI.2007.1013 [doi]
PST - ppublish
SO  - IEEE Trans Pattern Anal Mach Intell. 2007 Apr;29(4):738-42. doi: 
      10.1109/TPAMI.2007.1013.