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Brain signal reference concept using cross correlation based for brain computer interface
Hermanto B.R.a, Mengko T.R.a, Indrayanto A.a, Prihatmanto A.S.a
a School of Electrical Engineering and Informatic, 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]The sub system for EEG based brain computer interface system are feature extraction and classification. Translation from brain signal that contain a human intention into computer’s command or message should be accomplished with good feature extraction nor classification. One of the methods for analyzing brain signal in feature extraction process is using band power measurement from few or many EEG channels. Recalibration or daily calibration was needed in using brain computer interface instrument. In order to make the feature extraction more reliable, the proposed method is using brain signal reference. To develop brain signal reference, sampling method taken from brain signal when human intention occur. The next feature extraction will analyze whether the signal is similar with signal reference or not. Feature extraction will uses cross correlation method to measure the similarity. Based on the hypothesis that brain will produce the similar signal in the same intention, the brain signal reference concept is the method can be used for feature extraction process. It is also should be considered the level of similarity which it will transfer to classification process in estimating the intention. It is feasible using brain signal reference concept based on cross correlation 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]Classification process,Cross correlation methods,Cross correlations,Electroencephalograph (EEG),Feature extraction and classification,Human intentions,Reference concepts,Sampling 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=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Brain Computer Interface (BCI),cross correlation,Electroencephalograph (EEG),feature extraction,signal reference[/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.2013.6698531[/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]