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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ostadabbas, S. Jafari, R. |
| Copyright Year | 2010 |
| Description | Author affiliation: Embedded Systems and Signal Processing Lab, Department of Electrical Engineering, University of Texas at Dallas, Richardson, 75080, USA (Ostadabbas, S.; Jafari, R.) |
| Abstract | Analysis of Event Related Potentials (ERPs) produced by brain activities can provide insight into the timing of underlying brain function. ERPs can be classified by their time/frequency characteristics and spatial location on the scalp. Traditionally, ERPs are manually located by temporally and spatially averaged EEG signals. This process is error prone and sensitive to a priori assumptions. Our proposed algorithm is a general neuroscience-focused data mining algorithm that performs time and frequency analysis on ERPs and automatically extracts templates corresponding to Spectral Spatio-Temporal (SST) regions exhibiting significant differences between experimental outcomes. The method uses time-aligned templates, which preserve the characteristics of the signal important to cognitive researchers. The ability of the selected signal templates to differentiate between stimulus responses has been verified using a pattern recognition procedure. SST template extraction is tested on data taken from a Go/NoGo task and shown to both find relationships consistent with published neuroscience literature as well as novel relationships. |
| Starting Page | 4678 |
| Ending Page | 4682 |
| File Size | 975546 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424441235 |
| ISSN | 1557170X |
| DOI | 10.1109/IEMBS.2010.5626411 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-31 |
| Publisher Place | Argentina |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Correlation Solids |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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