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Heuristic Steady State Visual Evoked Potential based Brain Computer Interface system for robotic wheelchair application
Andronicus S.a, Harjanto N.C.a, Suprijantoa, Widyotriatmo A.a
a Faculty of Industrial Technology, Engineering Physics, Institute of Technology Bandung, 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]© 2015 IEEE.Development of Brain Computer Interface (BCI) system to enable connection of human intention directly with automated appliances without going through peripheral muscle these days is increasing as an effort to provide solution for patients with disabilities to do their activity normally. Unfortunately current BCI system still has shortage in complex configuration during electroencephalograph (EEG) measurement that uses large numbers of electrodes causing difficulties for the application of the BCI system especially for common usage. Therefore this study, we conduct research on Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface to design BCI system utilizing minimum amount of electrodes which is able to use for robotic wheelchair movement control. The BCI system is design heuristically based from the collected experiment data, utilizing banks of filter for the feature extraction and threshold-voting system for feature classification. Offline evaluation of the designed BCI system shows the average correct classification is 84,94% with Information Transfer Rate (ITR) is 68,9440 bits/min.[/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]Automated appliances,Complex configuration,Feature classification,Heuristic designs,Information transfer rate,Movement control,Robotic wheelchairs,Steady state visual evoked potentials[/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]Brain Computer Interface,heuristic design,minimum amount of electrode,robotic wheelchair movement control,Steady State Visual Evoked Potential[/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/ICICI-BME.2015.7401342[/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]