In this work, we propose new method beside the classic method, to control the motorized wheelchair using EOG signals. The new method allows the user to look around freely while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair; eye gazing and blinking. The user can still choose to control the wheelchair using the classic manual method in case the environment and obstacles structure does not help with the auto navigation method. In the new auto navigation method the microcontroller can know the goal point direction and distance by calculating the gaze angle that the user is gazing at. Gaze angle and blinks are measured and used as inputs for the controlling method. Tangent Bug algorithm is used to navigate the wheelchair in Auto controlling method.
This paper presents the characteristics of Electroencephalography (EEG) signal based on sensory modulation of visual stimuli assessments. The visual stimuli consisted of two assessments which are Phase 1 (the study phase) and Phase 2 (the working memory phase). The aim of this visual stimulus is to investigate the responses of the brain activity while remembering the sequence of the pictures. Based on this stimulus responses, EEG signal was recorded and captured from four channel locations at F8, F7, F4, and F3. This paper illustrates the extraction of EEG signals from normal children to determine the pattern of signal acquired. The data was analyzed using Wavelet Transform (WT) as normal children generally have beta (β) wave (14 -32 Hz). This study showed that the characteristics of EEG signal (beta wave) differ with respect to the type of visual stimuli assessment. As a result, Phase 2 had higher Direct Current (DC) value for all channels and the average beta wavelength for ten children was approximately 23.14 Hz.
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