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We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already...
This paper presents a study of classification and EEG feature improvement for a spatial auditory brain-computer interface (saBCI). This study provides a comprehensive test of a head-related impulse response (HRIR) cues for the saBCI speller paradigm. We present a comparison with previously developed HRIR-based spatial auditory modalities. We propose and optimize the three types of sound spatialization...
The paper presents results from a project aiming to create horizontally distributed surround sound sources and virtual sound images as auditory BCI (aBCI) stimuli. The purpose is to create evoked brain wave response patterns depending on attended or ignored sound directions. We propose to use a modified version of the vector based amplitude panning (VBAP) approach to achieve the goal. The so created...
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