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Signal Comparison of Developed EEG Device and Emotiv Insight Based on Brainwave Characteristics Analysis
Harke Pratama S.a, Rahmadhani A.a, Bramana A.a, Oktivasari P.a, Handayani N.b, Haryanto F.a, Suprijadia, Nurul Khotimah S.a
a Department of Physics, Faculty of Mathematic and Natural Sciences, Institut Teknologi Bandung, 40132, Indonesia
b Department of Physics, Faculty of Science and Technology, UIN Sunan Kalijaga, Yogyakarta, 55281, 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]© Published under licence by IOP Publishing Ltd.The usage of wireless system and dry electrode on electroencephalography (EEG) device becomes widely demanding, particularly in commercial purposes. While the wireless system is needed for lesser cable interference and practical function for mobility, the dry electrode is very important for signal consistency in longer period of brainwave acquisition. Previously, a wireless EEG device was developed in our laboratory; however, the evaluation of the acquired brainwave is needed for further usage and development. This research aimed to compare the signal acquired by the developed EEG device using Emotiv Insight device as a benchmark, which is already an established wireless and dry electrode-based EEG on the market. The brainwave acquisitions were conducted on the subject while resting with eyes closed. AF3 and AF4 of frontal lobe channels were used as the electrode placements. The results were then characterized using frequency band analysis, SNR comparison, and general signal inspection. The result showed that the signal patterns on both devices were visually similar. A minor difference on the amplitude scale can be adjusted by normalization method. The result of alpha band calculation, which is normally detected in resting activity, found similar on both devices. Furthermore, the SNR result from developed device was considered fairly close to the benchmarking device. This study showed that developed EEG device was considered comparable to Emotiv Insight in detecting alpha band extracted from resting frontal lobe, as well as in the brainwave filtering process and accuracy.[/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]Band calculation,Characteristics analysis,Electrode placement,Filtering process,Frequency band analysis,Normalization methods,Signal comparison,Wireless systems[/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][/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]This research was supported by Ministry of Research, Technology and Higher Education Republic of Indonesia through PTUPT scheme (contract no. 2/E1/KP.PTNBH/2019).[/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/10.1088/1742-6596/1505/1/012071[/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]