Method of identifying individuals using VEP signals and neural network
A method of identifying individuals using visual-evoked-potential (VEP) signals and neural network (NN) is proposed. In the approach, a backpropagation (BP) NN is trained to identify individuals using gamma-band (30-50 Hz) spectral power ratio of VEP signals extracted from 61 electrodes located on t...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2509/ http://shdl.mmu.edu.my/2509/1/1773.pdf |
| Summary: | A method of identifying individuals using visual-evoked-potential (VEP) signals and neural network (NN) is proposed. In the approach, a backpropagation (BP) NN is trained to identify individuals using gamma-band (30-50 Hz) spectral power ratio of VEP signals extracted from 61 electrodes located on the scalp of the brain. The gamma-band spectral-power ratio is computed using a zero-phase Butterworth digital filter and Parseval's time-frequency equivalence theorem. NN classification gives an average of 99.06% across 400 test VEP patterns from 20 individuals using 10-fold cross-validation scheme. This shows promise for the approach to be developed further as a biometric identification system. |
|---|