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A collaborative control of brain computer interface and robotic wheelchair
Widyotriatmo A.a, Suprijantoa, Andronicus S.a
a Instrumentation and Control, Industrial Technology Faculty, Bandung Institute of Technology, ITB, 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.This paper presents a new scheme in collaborating non-invasive brain computer interface (BCI) and a wheelchair equipped with robotic systems. The BCI system implements steady state visual evoked potential (SSVEP) method that extracts features from electro-encephalography (EEG) signals in determining the intentions or commands from human-brain. The classification of EEG signals utilizes filter-bank in accordance with frequency of the stimuli visual. The user intentions, which are to move ‘left’, ‘right’, and ‘forward’, and ‘stop’ are collaborated with an intelligent robotic system of a wheelchair. The wheelchair system is equipped with environment recognition sensors. The collaborative control is to manage the motion of the wheelchair based upon the intention of the user and the condition of the environment. The scenario is limited to the collaboration of BCI-robotic wheelchair in a corridor environment with walls on both sides. The user intention is set to ‘forward’. The intelligent robotic wheelchair perform wall following and obstacle avoidance motions while receiving the command ‘forward’. Also, it performs emergency-stop if the extracted intentions from the BCI put the user in dangerous situation. Experimental results are conducted to show the effectiveness of the proposed method.[/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][/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]collaborative control,EEG,mobile robot,non-invasive BCI,obstacle avoidance,wall-following,wheelchair[/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/ASCC.2015.7244838[/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]