BCI‐based control of electric wheelchair using fractal characteristics of EEG

Brain–computer interfaces (BCIs) have been attracting attention as interfaces that connect a brain to an external device. The implementation of a BCI requires establishing a technique that accurately recognizes the brain state, and a host of challenges remains for the implementation. This paper uses the emotion fractal analysis method (EFAM), which quantifies emotions based on data obtained from an electroencephalogram (EEG), to propose a BCI system that accurately discriminates and recognizes emotions such as delight, anger, sorrow, and pleasure, and uses that information to manipulate an electric wheelchair. The EFAM's emotion recognition rates for the four emotions, namely delight, anger, sorrow, and pleasure, were 81.11, 79.25, 73.16, and 85.42%, respectively, and its emotion isolation rates were 97.88, 98.08, 98.07, and 97.86%, respectively. Based on emotion data obtained in real time by the EFAM, we developed a novel BCI circuit that manipulates an electric wheelchair. Using this BCI circuit allows us to adjust the speed of an electric wheelchair in proportion to the intensity of the emotion.