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- MakaleAçık Erişim2025Intersession Robust Hybrid Brain–Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism
Aydın, Sefa; Melek, Mesut; Gökrem, Levent
This study introduces a hybrid Brain–Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user’s intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users’ changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability.
- MakaleAçık Erişim2025A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation
Aydin, Sefa; Melek, Mesut; Gökrem, Levent
Nowadays, brain–computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.
- 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.
- BildiriYalnız künye2018Power spectrum analysis of EEG for watching 2D & 3D videos and resting state
Manshouri, Negin; Maleki, Masoud; Kayikcioglu, Temel
Analysis of the brain waves has become useful over than 80 years. Along with the medical application, the brain signal is also used for other research works in the last fifteen years. In this study, by considering the depth factor in the image of a video, the effects of it are checked on EEG. A video is displayed in two-dimensional (2D) and three-dimensional (3D) format to the subjects. After recording the EEG signals in video watching, the resting state was recorded. Three states are considered for a better understanding of these effects on EEG. These states involve 2D&3D, 2D&resting and 3D&resting. Frequency analysis of the signals is evaluated by two methods. These methods are Fast Fourier Transform (FFT) and Welch method.
- 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.