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Implementation of small laplacian spatial filter for MU rhythm acqusition in Bci2000
Mubarok M.H.a, Mengko T.L.R.a, Prihatmanto A.S.a, Indrayanto D.A.a
a Department of Electrical Engineering, STEI Bandung Institute of Technology, 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]© 2014 IEEE.In a couple of decade there is many more technology that use brain signal as the control signal. One of this application is an input signal to a robot hand. Mu rhythm as the signal which associated with sensory motor activity will be used as the input signal. Moreover in this final project, there will be further explanation about the use of spatial filter and linear classifier to increase the performance of the controlled signal. The acqusition of mu rhythm will be firstly done by assuming the desired input channel which is done by the help of stimulus presentation a BCI2000 application. This assumed channel then analyze and act as an input for cursor task application. The spatial filter process was performed in this application and then the output of this spatial filter will be choosen as the input signal for cursor control signal in linear classifier, the performance of this classifier will be analyzed to increase its performance. The result showed that the implementation of the right spatial filter and the usage of linear classifier is necessary in order to acquiry and use mu rhythm as an input signal for BCI application. In addition it was found that the actual movement and imaginary movement produced a mu rhythm that could perform as an input for cursor task application.[/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]BCI,Cursor control,Linear classifiers,MU rhythm,Spatial filters,stimulus presentation[/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]BCI,cursor control,linear classifier,mu rhythm,spatial filter,stimulus presentation[/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/ICSEngT.2014.7111803[/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]