[vc_empty_space][vc_empty_space]
Signal reference selection and dimensionality reduction for crosscorrelation based feature extraction in EEG signals of brain computer interface
Hermanto B.R.a, Mengko T.R.a, Setijadi P.A.a, Indrayanto A.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Biomedical Engineering, Bandung, 40132, 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]© 2017 Pushpa Publishing House, Allahabad, India.In order to increase the accuracy of motor imagery signal detection in a brain computer interface system, choosing the right features is a key point. There are several methods for brain signal feature extraction. The most commonly used method uses features from the frequencydomain. The classification accuracy rate achieved by extracting features from the frequency-domain is quite good, but extracting particular features in the time-domain is still being explored as an alternative for getting better accuracy. Cross-correlation is a method for measuring the similarity between two signals and results in a cross-correlation sequence. Basic statistic parameters can be taken from the cross-correlation sequence as features of a signal and then used in a classification process. To achieve a high classification accuracy rate, we should choose the appropriate signal as the reference signal when applying crosscorrelation and also choose which basic statistic parameters to take as features. Model validation can be used for evaluating the reference signal and the basic statistic parameters in relation to the accuracy rate. The method proposed here for choosing the reference signal and the basic statistic parameters was applied and tested with the BCI Competition III, IVa dataset. Using 10-fold cross validation for model validation, the proposed method obtained a classification accuracy rate of 99%. This rate was attained by using the signal from channel CFC2 as the reference signal using the maximum value from the crosscorrelation sequence as the only feature. This result is approximately 4% better than the accuracy achieved in a previous study that used a similar method. Not only the accuracy rate was improved in this study, but it was also shown that the maximum value of the cross-correlation sequence could potentially be used as sole feature.[/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]Brain computer interface (BCI),Cross-correlation,Electroencephalograph (EEG),Fast fourier transform (FFT),Reference signal[/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/10.17654/EC017010185[/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]