{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:27:21Z","timestamp":1774034841040,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61807007"],"award-info":[{"award-number":["61807007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC2001100"],"award-info":[{"award-number":["2018YFC2001100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s circuit, and the high correlation coefficients (&gt;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 \u00b1 0.02, Berger effect was observed (p &lt; 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.<\/jats:p>","DOI":"10.3390\/s22051898","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:11:57Z","timestamp":1646079117000},"page":"1898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhilin","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Xingran","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Wang","family":"Wan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Zeguang","family":"Qin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}]},{"given":"Zhongze","family":"Gu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s13534-018-00093-6","article-title":"Wearable EEG and beyond","volume":"9","author":"Casson","year":"2019","journal-title":"Biomed. Eng. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2017.00398","article-title":"Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials","volume":"11","author":"Ratti","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13657","DOI":"10.1038\/s41598-018-32063-4","article-title":"Affective computing in virtual reality: Emotion recognition from brain and heartbeat dynamics using wearable sensors","volume":"8","author":"Greco","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Athavipach, C., Pan-Ngum, S., and Israsena, P. (2019). A Wearable In-Ear EEG Device for Emotion Monitoring. Sensors, 19.","DOI":"10.3390\/s19184014"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"04018050","DOI":"10.1061\/(ASCE)CO.1943-7862.0001506","article-title":"Measuring Workers\u2019 Emotional State during Construction Tasks Using Wearable EEG","volume":"144","author":"Hwang","year":"2018","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Looney, D., Park, C., Kidmose, P., Rank, M.L., Ungstrup, M., Rosenkranz, K., and Mandic, D.P. (September, January 30). An in-the-ear platform for recording electroencephalogram. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091733"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1513\/AnnalsATS.201605-342BC","article-title":"Wearable in-ear encephalography sensor for monitoring sleep preliminary observations from nap studies","volume":"13","author":"Looney","year":"2016","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TBME.2019.2911423","article-title":"Hearables: Automatic Overnight Sleep Monitoring with Standardized In-Ear EEG Sensor","volume":"67","author":"Nakamura","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cnp.2021.04.003","article-title":"Electrographic seizure monitoring with a novel, wireless, single-channel EEG sensor","volume":"6","author":"Frankel","year":"2021","journal-title":"Clin. Neurophysiol. Pract."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sopic, D., Aminifar, A., and Atienza, D. (2018, January 27\u201330). E-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures. Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy.","DOI":"10.1109\/ISCAS.2018.8351728"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gu, Y., Cleeren, E., Dan, J., Claes, K., Van Paesschen, W., Van Huffel, S., and Hunyadi, B. (2017). Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy. Sensors, 18.","DOI":"10.3390\/s18010029"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Van Hees, V.T., van Diessen, E., Sinke, M.R.T., Buitenhuis, J.W., van der Maas, F., Ridder, L., and Otte, W.M. (2018). Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Africa. bioRxiv, 324954.","DOI":"10.1101\/324954"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"213574","DOI":"10.1109\/ACCESS.2020.3040437","article-title":"HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1088\/0967-3334\/36\/7\/1469","article-title":"Measurement of neural signals from inexpensive, wireless and dry EEG systems","volume":"36","author":"Grummett","year":"2015","journal-title":"Physiol. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Williams, N.S., McArthur, G.M., and Badcock, N.A. (2020). 10 years of EPOC: A scoping review of Emotiv\u2019s portable EEG device. bioRxiv.","DOI":"10.1101\/2020.07.14.202085"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1016\/j.clinph.2017.09.115","article-title":"Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy\u2013A comparison with scalp EEG monitoring","volume":"128","author":"Zibrandtsen","year":"2017","journal-title":"Clin. Neurophysiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s41105-016-0078-2","article-title":"Inter-scorer reliability of sleep assessment using EEG and EOG recording system in comparison to polysomnography","volume":"15","author":"Nonoue","year":"2017","journal-title":"Sleep Biol. Rhythm."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices","volume":"22","author":"Katsigiannis","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cai, H., Sha, X., Han, X., Wei, S., and Hu, B. (2016, January 15\u201318). Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China.","DOI":"10.1109\/BIBM.2016.7822696"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102851","DOI":"10.1016\/j.autcon.2019.102851","article-title":"Pre-service fatigue screening for construction workers through wearable EEG-based signal spectral analysis","volume":"106","author":"Li","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1177\/1550059411435857","article-title":"EEG from a single-channel dry-sensor recording device","volume":"43","author":"Johnstone","year":"2012","journal-title":"Clin. EEG Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/TBME.2018.2835778","article-title":"Dry-Contact Electrode Ear-EEG","volume":"66","author":"Kappel","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pietto, M.L., Gatti, M., Raimondo, F., Lipina, S.J., and Kamienkowski, J.E. (2018). Electrophysiological approaches in the study of cognitive development outside the lab. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0206983"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-12-56","article-title":"Performance of the Emotiv Epoc headset for P300-based applications","volume":"12","author":"Duvinage","year":"2013","journal-title":"Biomed. Eng. Online"},{"key":"ref_25","first-page":"1","article-title":"Validation of the Emotiv EPOC\u00ae EEG gaming systemfor measuring research quality auditory ERPs","volume":"2013","author":"Badcock","year":"2013","journal-title":"PeerJ"},{"key":"ref_26","first-page":"1","article-title":"Validation of the Emotiv EPOC EEG systemfor research quality auditory event-related potentials in children","volume":"2015","author":"Badcock","year":"2015","journal-title":"PeerJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1111\/psyp.12888","article-title":"Acquiring research-grade ERPs on a shoestring budget: A comparison of a modified Emotiv and commercial SynAmps EEG system","volume":"54","author":"Barham","year":"2017","journal-title":"Psychophysiology"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hussain, I., and Park, S.-J. (2021). Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke. Brain Sci., 11.","DOI":"10.3390\/brainsci11070900"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dadebayev, D., Goh, W.W., and Tan, E.X. (J. King Saud Univ. Comput. Inf. Sci., 2021). EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques, J. King Saud Univ. Comput. Inf. Sci., in press.","DOI":"10.1016\/j.jksuci.2021.03.009"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"S116","DOI":"10.1111\/epi.16555","article-title":"Machine learning and wearable devices of the future","volume":"62","author":"Beniczky","year":"2021","journal-title":"Epilepsia"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gao, Z., Cui, X., Wan, W., and Gu, Z. (2019). Recognition of emotional states using multiscale information analysis of high frequency EEG oscillations. Entropy, 21.","DOI":"10.3390\/e21060609"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying Stable Patterns over Time for Emotion Recognition from EEG","volume":"10","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/8317357","article-title":"Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain","volume":"2017","author":"Zhuang","year":"2017","journal-title":"Biomed. Res. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101696","DOI":"10.1016\/j.artmed.2019.07.004","article-title":"Depression recognition using machine learning methods with different feature generation strategies","volume":"99","author":"Li","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_35","unstructured":"Shoeb, A.H., and Guttag, J.V. (2010, January 21\u201324). Application of machine learning to epileptic seizure detection. Proceedings of the ICML, Haifa, Israel."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.eswa.2019.05.057","article-title":"Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods","volume":"134","author":"Kaya","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2021.673955","article-title":"Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting","volume":"15","author":"Wan","year":"2021","journal-title":"Front. Hum. Neurosci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s12553-015-0113-3","article-title":"Portable electrocardiograph based on the integrated circuit ADS1294 using an android application as interface","volume":"5","author":"Balbinot","year":"2015","journal-title":"Health Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ijpsycho.2004.02.001","article-title":"Auditory event-related potential abnormalities in bipolar disorder and schizophrenia","volume":"53","author":"Vohs","year":"2004","journal-title":"Int. J. Psychophysiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1037\/0021-843X.108.1.120","article-title":"Context-processing deficits in schizophrenia: Converging evidence from three theoretically motivated cognitive tasks","volume":"108","author":"Cohen","year":"1999","journal-title":"J. Abnorm. Psychol."},{"key":"ref_41","first-page":"45","article-title":"Automatic identification and Removal of ocular artifacts from EEG using Wavelet transform","volume":"6","author":"Krishnaveni","year":"2006","journal-title":"Meas. Sci. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/89.928915","article-title":"Noise power spectral density estimation based on optimal smoothing and minimum statistics","volume":"9","author":"Martin","year":"2001","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2544","DOI":"10.1016\/j.clinph.2007.04.026","article-title":"The mismatch negativity (MMN) in basic research of central auditory processing: A review","volume":"118","author":"Paavilainen","year":"2007","journal-title":"Clin. Neurophysiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.ymssp.2007.06.001","article-title":"An algorithm for the continuous Morlet wavelet transform","volume":"21","year":"2007","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Circ. Physiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/978-1-137-01390-3_9","article-title":"Analysis of Variance (ANOVA)","volume":"Volume 6","author":"Acton","year":"2009","journal-title":"SPSS for Social Scientists"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1080\/01621459.1972.10481251","article-title":"Significance testing of the spearman rank correlation coefficient","volume":"67","author":"Zar","year":"1972","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s00034-009-9130-7","article-title":"Fast normalized cross-correlation","volume":"28","author":"Yoo","year":"2009","journal-title":"Circuits Syst. Signal. Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Katona, J., Ujbanyi, T., Sziladi, G., and Kovari, A. (2016, January 16\u201318). Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based brain-computer interface. Proceedings of the 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Wroclaw, Poland.","DOI":"10.1109\/CogInfoCom.2016.7804557"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.snb.2018.08.155","article-title":"Towards conductive-gel-free electrodes: Understanding the wet electrode, semi-dry electrode and dry electrode-skin interface impedance using electrochemical impedance spectroscopy fitting","volume":"277","author":"Li","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.clinph.2004.10.001","article-title":"Evaluation of commercially available electrodes and gels for recording of slow EEG potentials","volume":"116","author":"Tallgren","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"066035","DOI":"10.1088\/1741-2552\/ac4085","article-title":"Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene","volume":"18","author":"Faisal","year":"2021","journal-title":"J. Neural. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1038\/s41467-020-18503-8","article-title":"Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring","volume":"11","author":"Zhang","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"46016","DOI":"10.1088\/1741-2552\/abeeab","article-title":"Towards real-life EEG applications: Novel superporous hydrogel-based semi-dry EEG electrodes enabling automatically \u2018charge\u2013discharge\u2019 electrolyte","volume":"18","author":"Li","year":"2021","journal-title":"J. Neural. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51004","DOI":"10.1088\/1741-2552\/abbd50","article-title":"Review of semi-dry electrodes for EEG recording","volume":"17","author":"Li","year":"2020","journal-title":"J. Neural. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:29:27Z","timestamp":1760135367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":56,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051898"],"URL":"https:\/\/doi.org\/10.3390\/s22051898","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,28]]}}}