Data-Driven Multichannel Signal Processing for Brain Computer Interfacing

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Brain-computer interfacing is a technology that transmit information in the brain to the outside, has a variety of engineering and medial applications. To realize this technology, we have to establish fundamentals of signal processing for extracting necessary components from the observed electroencephalogram(EEG) and/or for removing unnecessary components. In this KAKENHI project, we paid attention into the empirical mode decomposition(EMD), which is a recently well-known data-driven signal decomposition technique, and we aimed at establishing methodologies that apply this promising technique to signal processing for BCI. As a result of this project, we have succeeded in an accurate separation of electrocardiogram(EOG) components from the observed EEG. Moreover, we have successfully developed a method for extracting necessary components from EEG signals during motor-imagery tasks. Additionally, we have developed and implemented on-line BCI platforms that utilize an Android smartphone and simple robots.