Abstract
The Brain-computer interface (BCI) is a technology that measures and analyzes electroencephalogram (EEG) signals to control robots or machines according to the user"s intention. The BCI is a beneficial technology that helps people who can not move their body because of paralysis or amputation so that they can control various electronic devices or express their intentions just by thinking. However, previous BCI technologies have a limitation that they can not have been unable to predict multiple intentions. To use the BCI system in daily life, BCI should be able to predict various intentions such as movement, communication, and robot control. In this study, we developed a BCI technology that can predict multiple intentions simultaneously using a convolutional neural network (CNN). We evaluated the proposed BCI model with EEG signals measured from 10 subjects participants during completing Sensory-Motor Rhythm (SMR) and Steady-State Visual Evoked Potential (SSVEP) tasks. The evaluation showed that the proposed BCI model could predict multiple intentions. The average accuracy averaged by all subjects was 62.8±13.7%. This result implies that BCI users can simultaneously perform various tasks, such as typing and moving. We expect that our study will promote the development of the practical applications of BCI. in daily life.
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