P300 Feature Extraction of Visual and Auditory Evoked EEG Signal
| Autor: | Xiao Yan Qiao, Jia Hui Peng |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science Noise (signal processing) Speech recognition Interface (computing) Feature extraction Wavelet transform Pattern recognition General Medicine Electroencephalography Signal Feature (computer vision) medicine Artificial intelligence Evoked potential business |
| Zdroj: | Applied Mechanics and Materials. :1374-1377 |
| ISSN: | 1662-7482 |
| Popis: | It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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