Information-preserving feature filter for short-term EEG signals

Abstract The brain-computer interface (BCI) has become one of the most important biomedical research fields and has many useful applications. An important component of BCI, electroencephalography (EEG) is in general sensitive to noise and rich in all kinds of information from our brain. In this paper, we study the feature fusion problem in electroencephalography (EEG). We introduce (1) a discriminative feature extractor which can classify multi-labels from short-term EEG signals, and (2) a new strategy to filter out unwanted features from EEG signals based on our feature extractor. Filtering out signals relating to one property of the EEG signal while retaining another is similar to the way we can listen to just one voice during a party, which is known as the cocktail party problem in the machine learning area. Built based on the success of short-term EEG discriminative model, the feature filter is an end-to-end framework which is trained to map EEG signals with unwanted features directly to EEG signals without those features. Our experimental results on an alcoholism dataset show that our novel model can filter out over 90% of alcoholism information on average from EEG signals, with an average of only 4.2% useful feature accuracy lost, showing effectiveness for our proposed task.

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