Brain–Computer-Interface-Based Smart-Home Interface by Leveraging Motor Imagery Signals

In this work, we propose a brain–computer-interface (BCI)-based smart-home interface which leverages motor imagery (MI) signals to operate home devices in real-time. The idea behind MI-BCI is that different types of MI activities will activate various brain regions. Therefore, after recording the user’s electroencephalogram (EEG) data, two approaches, i.e., Regularized Common Spatial Pattern (RCSP) and Linear Discriminant Analysis (LDA), analyze these data to classify users’ imagined tasks. In such a way, the user can perform the intended action. In the proposed framework, EEG signals were recorded by using the EMOTIV helmet and OpenVibe, a free and open-source platform that has been utilized for EEG signal feature extraction and classification. After being classified, such signals are then converted into control commands, and the open communication protocol for building automation KNX (“Konnex”) is proposed for the tasks’ execution, i.e., the regulation of two switching devices. The experimental results from the training and testing stages provide evidence of the effectiveness of the users’ intentions classification, which has subsequently been used to operate the proposed home automation system, allowing users to operate two light bulbs.