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Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device
The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simul...
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Published in: | Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (5), p.1898 |
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description | The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements. |
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In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s22051898</identifier><identifier>PMID: 35271044</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Analysis ; attention ; Audiology ; Auditory tasks ; Brain ; Brain research ; Correlation coefficients ; Cross correlation ; EEG ; Electrodes ; Electroencephalography ; Electroencephalography - methods ; Emotions ; Epilepsy ; ERP ; Evoked Potentials ; Experiments ; Frontal Lobe ; Investigations ; Laboratories ; Machine learning ; Monitoring systems ; Recognition, Psychology ; Research methodology ; rest ; Signal processing ; Signal quality ; Sleep ; wearable ; Wearable computers ; Wearable Electronic Devices ; Wearable technology</subject><ispartof>Sensors (Basel, Switzerland), 2022-02, Vol.22 (5), p.1898</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>attention</subject><subject>Audiology</subject><subject>Auditory tasks</subject><subject>Brain</subject><subject>Brain research</subject><subject>Correlation coefficients</subject><subject>Cross correlation</subject><subject>EEG</subject><subject>Electrodes</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Emotions</subject><subject>Epilepsy</subject><subject>ERP</subject><subject>Evoked Potentials</subject><subject>Experiments</subject><subject>Frontal Lobe</subject><subject>Investigations</subject><subject>Laboratories</subject><subject>Machine learning</subject><subject>Monitoring systems</subject><subject>Recognition, Psychology</subject><subject>Research methodology</subject><subject>rest</subject><subject>Signal processing</subject><subject>Signal quality</subject><subject>Sleep</subject><subject>wearable</subject><subject>Wearable computers</subject><subject>Wearable Electronic Devices</subject><subject>Wearable technology</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptklFv0zAQgCMEYmPwwB9AkXiBhw7bZ8f2C9I02lGpAiFAPFqX5BJcpfHmJEX797jrKCtCfrB1_u6z73RZ9pKzcwDL3g1CMMWNNY-yUy6FnJkUePzgfJI9G4Y1YwIAzNPsBJTQnEl5mi2--rbHLv8yYefH23zZb2kYfYujD30emhzzT_Qr_0EYsewoX8TQj4lfhZLy-fwq_0BbX9Hz7EmD3UAv7vez7Pti_u3y42z1-Wp5ebGaVQqKccalRlNqzQhqW3NrCJhRxBptLQhBTS13nyyVQVQFCsFLIU2tNRhhmTBwli333jrg2l1Hv8F46wJ6dxcIsXUYR1915IALBCw1t7yQBRfWWK6oRKlKksaq5Hq_d11P5YbqivoxYnckPb7p_U_Xhq1LImkNJMGbe0EMN1Nqm9v4oaKuw57CNDhRgNEcCiUT-vofdB2mmBp_R2ltNAP1l2oxFeD7JqR3q53UXWjDC84EKxJ1_h8qrZo2vgo9NT7FjxLe7hOqGIYhUnOokTO3GyB3GKDEvnrYlAP5Z2LgN5owulU</recordid><startdate>20220228</startdate><enddate>20220228</enddate><creator>Gao, Zhilin</creator><creator>Cui, Xingran</creator><creator>Wan, Wang</creator><creator>Qin, Zeguang</creator><creator>Gu, Zhongze</creator><general>MDPI AG</general><general>MDPI</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20220228</creationdate><title>Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device</title><author>Gao, Zhilin ; 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In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35271044</pmid><doi>10.3390/s22051898</doi><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Analysis attention Audiology Auditory tasks Brain Brain research Correlation coefficients Cross correlation EEG Electrodes Electroencephalography Electroencephalography - methods Emotions Epilepsy ERP Evoked Potentials Experiments Frontal Lobe Investigations Laboratories Machine learning Monitoring systems Recognition, Psychology Research methodology rest Signal processing Signal quality Sleep wearable Wearable computers Wearable Electronic Devices Wearable technology |
title | Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device |
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