2/20/2023 0 Comments Eeg sensor arduinoThe prosthetic arm module designed consists of Arduino coupled with servo motors to perform the command. The extracted brain signals act as command signals that are transmitted to the Microcontroller via RF medium. BCI system consists of an EEG sensor to capture the brain signal, which will be processed using ThinkGear module in MATLAB. In this paper, EEG-based brain controlled prosthetic arm has been developed using BCI with the help of Neurosky Mindwave headset to yield the two main movements of fingers in the arm: Flexion and Extension. EEG-based brain controlled prosthetic arm is a non-invasive technique that can serve as a powerful aid for severely disabled people in their daily life, especially to help them move. But it has certain disadvantages like it relies on the nerves to be undamaged and it's very expensive, which can be overcome by EEG-based brain controlled prosthetic arm. Lately myoelectric prosthetics are in use. The module was successful at presenting the paradigm in all the trials and in indentifying the events of each trial in the signals that were recorded. The module was tested in the EEG acquisition of motor imagery tasks in a BCI research protocol involving thirty healthy subjects. Furthermore, an average difference of 167 μs was obtained between the time intervals theoretically set for every event and the time intervals obtained. In the module validation, a delay of 1 ± 0.5 ms between the time in which the microcontroller marks the event in the EEG amplifier and the time in which the event is showed to the subject in the computer's monitor was measured. the test subject to perform the motor imagery tasks and an algorithm aimed to extract the EEG information related to the motor imagery tasks. The device components are: a microcontroller which sets the time intervals of the events of a Graz type paradigm, and sends markers to an EEG acquisition system a software that presents the visual and auditory clues of the paradigm in a personal computer (PC) for. This work presents a module that aims to facilitate the acquisition of motor imagery tasks in electroencephalography (EEG) research.
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