Abstract
A parameter estimation and classification fusion approach is developed to classify averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble. The parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Very importantly, it is shown that parametric classifiers can be designed and evaluated without having to collect a prohibitively large number of single-trial ERPs. It is also shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 163-164 |
| Number of pages | 2 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 1 |
| State | Published - 2002 |
| Externally published | Yes |
| Event | Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States Duration: Oct 23 2002 → Oct 26 2002 |
Keywords
- Classification
- Event-related potentials
- Fusion
- Parameter estimation
- Signal averaging
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics