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Design of Brain-computer interface platform for semi real-time commanding electrical wheelchair simulator movement
Kaysa W.A.a, Suprijanto S.a, Widyotriatmo A.a
a Engineering Physics Program, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Brain Computer Interface (BCI) research has developed intensively during past years, especially in the assistive technology development for disabled people, e.g. electric wheelchair. This paper will propose our design of the BCI platform for commanding the electric wheelchair simulator. A wireless EEG device is used because of the need for mobile, comfortable, and user-friendly BCI platform. This paper will cover the design process to build the BCI platform prototype, and evaluation about the result achieved. Our BCI platform consists of Emotiv EPOC wireless EEG, and a computer for EEG acquisition, noise filtering, feature extraction, feature classification, and gives control command to wheelchair simulator that is connected via USB. Power spectral density is used as feature extraction of μ rhythm and artificial neural network with multi layer perceptron is used for classifying the idle and movement condition and for classifying between right hand and left hand movement. The first prototype of our BCI system has an interchangeable classification database that allows drastic modification of the system input, output, user recognition, and even the classification method. © 2013 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Assistive technology,Classification methods,Disabled people,Eeg acquisitions,Electric wheelchair,Feature classification,Multi layer perceptron,Real time[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Electric Wheelchair,Electro-encephalography(EEG),Semi Real Time BCI[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2013.6734043[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]