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.

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.

In this study, a six-leg spider robot (hexapod) was designed and controlled for greenhouse, search and rescue operations and military applications. Solidworks design program was used in the design stage and Matlab Simulink program was used in the control stage of the robot. In the study, a motion trajectory was determined by interpolation technique for the robot and it was aimed to move on this trajectory. Proportional-Integral-Differential (PID) controller and Fuzzy Logic Controller (FLC) were used for the trajectory control of the robot. The robot walk movements were applied in both control types with motion on flat ground, motion on inclined ground, motion at different weights and different friction coefficients. In the analysis studies, the total weight of the robot was taken into consideration as 4kg. As a result of the analysis studies, it was observed that this robot, which was designed and analyzed, followed a trajectory defined by a mass of 4 kg with an error value of 1 mm on average. During the trajectory tracking, it was found that the Fuzzy controller performs better than the PID controller at the turning points of the reference trajectory curve of the robot.

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 the LED was off. The recorded data were processed using a Butterworth filter and 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 ITR value of 36.75 (bits/min) were achieved using the RF classifier.

This study introduces, a hybrid BCI system with a robust and secure activation mecha-nism between sessions is introduced, aiming to minimize the negative effects of visual stimulus-based Brain-Computer Interface (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 7Hz for safe ac-tivation and objects moving in different directions. While the LED functions as an activa-tion 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 col-lected from 15 participants in two different sessions. Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. 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%. The results show that the proposed system offers a safe activation infrastructure that can effectively adapt to users' changing cognitive states throughout the day by reducing visual fatigue, along with its high accuracy and stability.

Günümüzde, doğuştan veya omurilik yaralanması gibi sinir sistemi problemleri sebebiyle tamamen felç olmuş bireylerin günlük ihtiyaçlarını karşılayabilmeleri için Beyin-bilgisayar arayüz (BBA) sistemleri geliştirilmiştir. BBAlar, çeşitli cihazları kullanarak topladığı beyin sinyallerini bilgisayarlar aracılığıyla işleyerek, çevre cihazları harekete geçirmek için çıktı komutları üreten sistemlerdir. Fakat BBA sistemlerinin kullanıcılar üzerinde bazı olumsuz etkileri vardır. Bu çalışmada, Görsel uyaran potansiyel (GUP) ve P300 gibi görsel uyaran tabanlı BBA sistemlerinde kullanılan farklı frekans değerlerinde titreşen uyaranların sistem kullanıcılarının göz sağlığı üzerinde meydana getirdiği olumsuz etkiyi azaltmak amacıyla; hareketli nesneler yaklaşımını kullanan bir İnsan-Makine Arayüz (İMA) sistemi önerilmiştir. Sistem, kullanıcının hareketli topları odaklanarak takip etmesi sonucunda Elektrosefalografi (EEG) sinyallerinde meydana gelen Elektrookülografi (EOG) artefaktlarını kullanarak hareket yörüngelerini makine öğrenme algoritmaları ile sınıflandırmayı amaçlamaktadır. Çalışma, Emotiv EPOC EEG cihazı aracılığıyla dört farklı rotada hareket eden toplar içeren yaklaşım kullanılarak sağlıklı 8 denek üzerinde gerçekleştirilmiştir. Kaydedilen ham verilere ön işleme aşamaları uygulanmıştır ve etkin kanal seçimi yapılmıştır. Tespit edilen etkin kanallara (AF3, AF4) ait verilere Güç Spektral Yoğunluğu (GSY) ve Ayrık Dalgacık Dönüşümü (ADD) yöntemleri uygulanmıştır. Her yöntem için öznitelikler çıkartılmış ve k-en yakın komşu (k-EYK) ve Lineer Diskriminant Analiz (LDA) algoritmaları kullanılarak sınıflandırılmıştır. GSY yöntemi için LDA ve k-EYK algoritmaları ile sırasıyla 93.86%, 92.62% doğruluk oranları ve 31.42 (bit/dk), 30.10 (bit/dk) ITR değerleri hesaplanmıştır. ADD yöntemi için ise sırasıyla 92.84%, 92.17% doğruluk oranları ve 30.36 (bit/dk), 29.62 (bit/dk) ITR değerleri elde edilmiştir. Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.