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- MakaleYalnız künye2021Brain-computer interface systems for smart homes-a review study
Maleki, Masoud; Manshouri, Negin; Kayikcioglu, Temel
Brain-computer Interface (BCI) systems, usually using signals taken from users' brain through electroencephalography (EEG), control various devices around and provide the user's com-mand by interacting. Improving the quality of life of people with disabilities is the main goal of BCI systems. The importance of BCI-based smart home systems is further increasing as a smart home system directly affects the life of a disabled individual. On the other hand, few BCI systems can be run directly using smart home systems. The importance of the BCI-based smart home and the few existing systems require more work in this vital field. In addition, no reviews have described BCI systems in a smart home. In this study, we reviewed all the papers on BCI-based smart home systems published in the last 6 years. These studies investigated and evaluated BCI systems from nine different perspectives. In addition, all studies were examined in terms of signal processing methods. Finally, the problems and challenges of these systems were discussed and new solutions were pro-posed.
- MakaleAçık Erişim2020Low-cost brain-computer interface using the emotiv epoc headset based on rotating vanes
Melek, Mesut; Manshouri, Negin; Kayikcioglu, Temel
In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.
- BildiriYalnız künye2018Fast and accurate classifier-based brain-computer interface system using single channel EEG data
Maleki, Masoud; Manshouri, Negin; Kayikcioglu, Temel
In brain-computer interface system (BCIs), direct communication between humans and computers is performed by analyzing neural signals and transforming them into digital signals. In the continuation of our previous works, in this paper, we advanced the proposed BCI system that was based on the gaze on rotating vanes. The speed of communication and convenience of the user are very important factors in BCI systems. Therefore, in this paper, for the convenience of the user, a single EEG channel was used. Also, for to increase the speed of the system, we tried to the classification of 0.5 sec epochs with a partial least squares regression (PLSR) as a fast classifier. In addition, we computed the information transfer rate (ITR) that has proved our proposed BCI system is fast and accurate. This system could be used in real-time implementations due to having high classification rate, speed and convenience of the user.