Extracting single trial visual evoked potentials using selective eigen-rate principal components
In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
2005
|
| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2370/ |
| _version_ | 1848790037396717568 |
|---|---|
| author | Andrews, S, S Kamel, , N Palaniappan, R, R |
| author_facet | Andrews, S, S Kamel, , N Palaniappan, R, R |
| author_sort | Andrews, S, S |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli. |
| first_indexed | 2025-11-14T18:06:15Z |
| format | Article |
| id | mmu-2370 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:06:15Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-23702011-08-23T01:29:01Z http://shdl.mmu.edu.my/2370/ Extracting single trial visual evoked potentials using selective eigen-rate principal components Andrews, S, S Kamel, , N Palaniappan, R, R QA75.5-76.95 Electronic computers. Computer science In single trial analysis, when using Principal Component Analysis (PCA) to extract Visual Evoked Potential (VEP) signals, the selection of principal components (PCs) is an important issue. We propose a new method here that selects only the appropriate PCs. We denote the method as selective eigen-rate (SER). In the method, the VEP is reconstructed based on the rate of the eigen-values of the PCs. When this technique is applied on emulated VEP signals added with background electroencephalogram (EEG), with a focus on extracting the evoked P3 parameter, it is found to be feasible. The improvement in signal to noise ratio (SNR) is superior to two other existing methods of PC selection: Kaiser (KSR) and Residual Power (RP). Though another PC selection method, Spectral Power Ratio (SPR) gives a comparable SNR with high noise factors (i.e. EEGs), SER give more impressive results in such cases. Next, we applied SER method to real VEP signals to analyse the P3 responses for matched and non-matched stimuli. The P3 parameters extracted through our proposed SER method showed higher P3 response for matched stimulus, which confirms to the existing neuroscience knowledge. Single trial PCA using KSR and RP methods failed to indicate any difference for the stimuli. 2005 Article NonPeerReviewed Andrews, S, S and Kamel, , N and Palaniappan, R, R (2005) Extracting single trial visual evoked potentials using selective eigen-rate principal components. ENFORMATIKA, VOL 7: IEC 2005 PROCEEDINGS. pp. 330-333. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Andrews, S, S Kamel, , N Palaniappan, R, R Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title | Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title_full | Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title_fullStr | Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title_full_unstemmed | Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title_short | Extracting single trial visual evoked potentials using selective eigen-rate principal components |
| title_sort | extracting single trial visual evoked potentials using selective eigen-rate principal components |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2370/ |