A Comparison of Classifiers in a Task Consisting of Classifying Single Visual Event-Related Cortical Potentials in Humans
| Autor: | A. V. Zakharov, V. A. Bulanov, M. S. Sergeeva, S. N. Agapov |
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| Rok vydání: | 2018 |
| Předmět: |
Signal processing
medicine.diagnostic_test Event (computing) Computer science business.industry General Neuroscience Pattern recognition 02 engineering and technology Electroencephalography Blind signal separation Task (project management) 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Neuroscience and Behavioral Physiology. 48:1140-1144 |
| ISSN: | 1573-899X 0097-0549 |
| DOI: | 10.1007/s11055-018-0678-1 |
| Popis: | The development of neurocomputer interfaces (NCI) using the EEG requires use of effective algorithms for signal analysis. One approach to creating NCI is based on use of the characteristics of individual visual event-related potentials (VEP) for control purposes. However, this is a difficult task requiring a combination of different approaches related to signal processing, particularly blind decomposition of sources (blind source separation), machine learning, and various others. We present here results from a comparative analysis of several classifiers in a task consisting of recognition of individual VEP. Open-access EEG traces were used for this study. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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