Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
| Autor: | Hava T. Siegelmann, Gideon F. Inbar, Hillel Pratt, D.H. Lange |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Computer science
Models Neurological Ensemble averaging Biomedical Engineering Stimulus (physiology) Electroencephalography Machine learning computer.software_genre medicine Humans Learning Computer Simulation Evoked Potentials Quantitative Biology::Neurons and Cognition medicine.diagnostic_test Artificial neural network business.industry Brain Winner-take-all medicine.anatomical_structure Neural Networks Computer Neuron Artificial intelligence Nerve Net Artifacts business computer |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 47:822-826 |
| ISSN: | 0018-9294 |
| DOI: | 10.1109/10.844236 |
| Popis: | Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. The authors' method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research. |
| Databáze: | OpenAIRE |
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