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[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) is a system that directly utilize Electroencephalograph (EEG) signals to control external devices without aid from any limb of the body. BCI system consists of brainwave acquisition, signal processing, feature extraction and classification. A design of BCI system has been developed by using a wireless EEG Emotiv EPOC neuroheadset and OpenViBE. Both of them are open-source system which gives opportunity to develop our BCI system freely. Mu wave is extracted from the acquired brainwaves when the subject imagined hand movement. Mu wave can be obtained on FC5 and FC6, where premotor activities take place, by apply it to a 8 – 13 Hz bandpass filter. Mu wave power which is the square of EEG signal amplitude is extracted to be classified into two different classes. Feature classification is done by using Support Vector Machine (SVM) in offline classification and online training. EEG signal was acquired on three healthy subjects without well training with BCI control. The task of subjects are imaginary movement of right and left hand with stimulation by a left and right arrow on the screen. Configuration for training and testing phase has been successfully done in OpenViBE towards online application. The mean recognition rate in offline testing and single trial classification is 60.63% for right arrow and 45.93% for left arrow on all subjects. © 2012 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]Feature classification,Feature extraction and classification,Imaginary movements,Offline classification,On-line applications,Open source system,Single-trial classifications,Training and testing[/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,Mu wave,support vector machine,wireless EEG[/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/CCSII.2012.6470489[/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]