The article describes the design process of building a hybrid brain-computer interface based on Electrooculography (EOG) and centre eye tracking. In the first paragraph authors presented theoretical information about Electroencephalography (EEG), Electrooculography (EOG), and Eye. Authors prepared an overview of the literature concerning hybrid BCIs. The interface was built with use of bioactive sensors mounted on the head. Movement of industrial robot model was triggered by a signal from eyes movement by EOG and eye tracking. The built interface has been tested. Three experiments were carried out. In all experiments, three people aged 25-35 were involved. 30 attempts per scenario were recorded. Between each attempt, a respondent had a 1-minute break. The investigators attempted to move cube from one table to the other.

Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EEG signal recording devices and ever-lower set prices. Unfortunately, such systems are substantially limited in terms of the number of generated commands. This especially applies to sets that are not medical devices. This article proposes a hybrid brain-computer system based on the Steady-State Visual Evoked Potential (SSVEP), EOG, eye tracking, and force feedback system. Such an expanded system eliminates many of the particular system shortcomings and provides much better results. The first part of the paper presents information on the methods applied in the hybrid brain-computer system. The presented system was tested in terms of the ability of the operator to place the robot’s tip to a designated position. A virtual model of an industrial robot was proposed, which was used in the testing. The tests were repeated on a real-life industrial robot. Positioning accuracy of system was verified with the feedback system both enabled and disabled. The results of tests conducted both on the model and on the real object clearly demonstrate that force feedback improves the positioning accuracy of the robot’s tip when controlled by the operator. In addition, the results for the model and the real-life industrial model are very similar. In the next stage, research was carried out on the possibility of sorting items using the BCI system. The research was carried out on a model and a real robot. The results show that it is possible to sort using bio signals from the human body.

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The research aim was to analyse the influence of velocity and size of markers on the accuracy of motion capture measurement utilising image processing with the use of OpenCV. On the basis of the obtained results, the usefulness of the applied measurement method in studying the kinematics of the human body while driving operating a wheelchair was determined. This article presents the test results for a low-budget motion capture measurement system for testing the kinematics of the human body in a single plane. The tested measuring system includes a standard activity camera Xiaomi Yi4K, expanded polystyrene markers with printed ArUco codes, and original software for marker position detection developed by the author. The analysis of the measurement method with regard to its applicability in biomechanical studies has highlighted several key factors: the number of measuring points, measurement accuracy expressed as a relative error and the limit velocity at which the marker trajectory is correctly represented. The article shows that the limit velocity of the marker is 2.2 m/s for 50x50 mm markers and 1.4 m/s for 30x30 mm markers. The number of measured points ranged from 233 to 2,457 depending on the marker velocity. The relative error did not exceed 5% for the marker velocities and thus provided a correct representation of its trajectory.