A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task
This work addresses the need for more user-friendly and accurate BCI systems for smart home control, representing an incremental improvement over existing methods.
The study tackled the problem of low accuracy and poor intuitiveness in brain-computer interface (BCI) smart home systems by proposing a new paradigm combining event-related potentials and speech imagery tasks, achieving a decoding accuracy of 88.1% (±5.90), which is significantly higher than conventional ERP systems.
Recently, smart home systems based on brain-computer interface (BCI) has attracted a wide range of interests in both industry and academia. However, the current BCI system has several shortcomings as it produces a comparatively lower accuracy for real-time implementations as well as the intuitive paradigm for the users cannot be well established here. Therefore, in this study, we proposed a highly intuitive BCI paradigm that combines event-related potential (ERP) with the speech-imagery task for the individual target objects. The decoding accuracy of the proposed paradigm was 88.1% (plus or minus 5.90) which is a much significant higher performance than a conventional ERP system. Furthermore, the amplitude of N700 components was significantly enhanced over frontal regions which are priory evoked by the speech-imagery task. Our results could be utilized to develop a smart home system so that it could be more user-friendly and convenient by means of delivering user's intentions both, intuitively and accurately.