Abstract: One of the key objectives of brain-computer interface (BCI) design is to construct accurate electroencephalogram (EEG) based classifier. But out of laboratory all EEG signals are contaminated with artifacts, which hamper algorithmic processing and EEG analysis, i.e. classifier ought to get a prediction for noisy data. Real-time BCI system rely on relatively clean EEG signals. Therefore, the exclusion of artifacts is of special interest for BCI applications in everyday life. There are two main approaches to this objective: automatic EEG artifact...
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Topics: 
Artificial intelligence
Speech recognition