  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.
