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Identification of alertness state based on EEG signal using wavelet extraction and neural networks
Djamal E.C.a, Suprijantob, Arif A.a
a Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia
b Department of Engineering Physics, Institut Teknologi Bandung, 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.This research proposed identification of alertness state using wavelet transformation and neural networks. Wavelet extracted three wave components of an electroencephalogram (EEG) signal, namely alpha, beta and theta as input of neural networks, so that reduce 256 Hz recording into 28 data each second. It also used a asymmetric of the channel that improved recognition. EEG signals was obtained from four subject as training data which was tested to other four subjects. Each subject recorded with blue light stimulation during eight minutes and continued no stimulation in last eight minutes. It was sit position. Light stimulation was intended to increase alertness the subject. This research focused on develop identification of alertness state system. Based on the results of testing the system on the new data showed that C3 and C4 channel made best classification with 81% recognition accuracy. From all data, less alert state gave recognition more than other. Characteristic of data was significant factor of the result.[/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]alertness state,EEG signals,Electroencephalogram signals,Light stimulation,Recognition accuracy,Wave components,Wavelet extractions,Wavelet transformations[/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]alertness state,EEG signal,identification,neural network,wavelet extraction[/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/IC3INA.2014.7042623[/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]