BROAD APPLICATIONS OF CONSUMER-GRADE EEG DEVICES: FROM CLINICAL DIAGNOSTICS TO BRAIN-COMPUTER INTERFACES AND SOCIAL SCIENCES – LITERATURE REVIEW
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5446Keywords:
Consumer-Grade EEG, Wireless EEG Devices, Digital Biomarkers, Mobile HealthAbstract
Research Objective: This review article aims to assess the rapidly growing market for commercial electroencephalography (EEG) devices and analyze their transition from sterile laboratory conditions to a variety of real-world applications. The paper examines the technological evolution of wireless systems and their effectiveness in clinical, social, and engineering fields.
Methods: This article synthesizes the current scientific literature on mobile EEG platforms, such as the Emotiv and Muse systems, and Ear-EEG technologies. Applications are classified by clinical specialties, behavioral monitoring, and novel brain-computer interface (BCI) technologies, while also assessing validation studies against the "gold standard" of medical equipment.
Results: The analysis demonstrates that modern consumer-grade EEG devices, supported by machine learning algorithms and artificial intelligence, provide high diagnostic accuracy in detecting sleep disorders, neurodegenerative markers (Alzheimer's and Parkinson's disease), and psychiatric conditions (anxiety, depression, ADHD). Furthermore, the review identifies key advances in emergency neurology, addiction monitoring, and intraoperative safety. Emerging fields such as ethical neuromarketing and BCI-guided assistive robotics demonstrate the technology's potential to improve human-machine interaction and social inclusion for people with disabilities.
Conclusions: Commercial EEG devices represent a paradigm shift in personalized healthcare and social science research. Despite persistent challenges related to signal artifacts and data privacy, the democratization of neurotechnology enables continuous monitoring with high ecological validity. Integrating these devices into everyday life offers unprecedented opportunities for early diagnosis, non-pharmacological interventions, and the development of "digital biomarkers” ultimately transforming brain health management and decision-making in the digital age.
References
Ajčević, M., Furlanis, G., Naccarato, M., Polverino, P., Marsich, A., Sulligoi, G., & Manganotti, P. (2021). Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis-treated ischemic stroke: A wireless EEG study. Medical & Biological Engineering & Computing, 59(1), 121–129. https://doi.org/10.1007/s11517-020-02280-z
American Academy of Sleep Medicine. (2015, June 1). Seven or more hours of sleep per night: A health necessity for adults. https://aasm.org/seven-or-more-hours-of-sleep-per-night-a-health-necessity-for-adults/
Baghdadi, A., Aribi, Y., Fourati, R., Halouani, N., Siarry, P., & Alimi, A. M. (2019). DASPS: A database for anxious states based on a psychological stimulation. arXiv. https://doi.org/10.48550/arXiv.1901.02942
Bashivan, P., Rish, I., & Heisig, S. (2016). Mental state recognition via wearable EEG. arXiv. https://doi.org/10.48550/arXiv.1602.00985
Braley, T. J., & Boudreau, E. A. (2016). Sleep disorders in multiple sclerosis. Current Neurology and Neuroscience Reports, 16(5), Article 50. https://doi.org/10.1007/s11910-016-0649-2
Brandt, R., Park, M., Wroblewski, K., Arana, A. C., Isola, M. S., & Tansey, M. (2021). Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes. Diabetologia, 64(10), 2159–2169. https://doi.org/10.1007/s00125-021-05500-9
Chabin, T., Gabriel, D., Haffen, E., Moulin, T., & Pazart, L. (2020). Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure? PLOS ONE, 15(12), Article e0244820. https://doi.org/10.1371/journal.pone.0244820
De Blasio, F. M., Love, S., Barry, R. J., Wassink, K., Cave, A. E., Armour, M., & Steiner-Lim, G. Z. (2023). Frontocentral delta-beta amplitude coupling in endometriosis-related chronic pelvic pain. Clinical Neurophysiology, 149, 146–156. https://doi.org/10.1016/j.clinph.2023.02.173
Dey, E., & Roy, N. (2022). OMAD: On-device mental anomaly detection for substance and non-substance users. arXiv. https://doi.org/10.48550/arXiv.2204.07038
Di Gruttola, F., Malizia, A. P., D’Arcangelo, S., Lattanzi, N., Ricciardi, E., & Orfei, M. D. (2021). The relation between consumers’ frontal alpha asymmetry, attitude, and investment decision. Frontiers in Neuroscience, 14, Article 577978. https://doi.org/10.3389/fnins.2020.577978
Diouri, O., Cigler, M., Vettoretti, M., Mader, J. K., Choudhary, P., & Renard, E. (2021). Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments. Diabetes/Metabolism Research and Reviews, 37(7), Article e3449. https://doi.org/10.1002/dmrr.3449
Dunn, K. E., Finan, P. H., Huhn, A. S., Gamaldo, C., Bergeria, C. L., & Strain, E. C. (2022). Wireless electroencephalography (EEG) to monitor sleep among patients being withdrawn from opioids: Evidence of feasibility and utility. Experimental and Clinical Psychopharmacology, 30(6), 1016–1023. https://doi.org/10.1037/pha0000483
Emotiv Inc. (n.d.-a). EPOC X: 14-channel wireless EEG headset. Retrieved March 19, 2026, from https://www.emotiv.com/pl/epoc-x
Emotiv Inc. (n.d.-b). MN8: Enterprise brain-sensing earbuds. Retrieved March 19, 2026, from https://www.emotiv.com/pl/mn8
Encyclopaedia Britannica. (n.d.-a). Sleep. Retrieved March 19, 2026, from https://www.britannica.com/science/sleep
Encyclopaedia Britannica. (n.d.-b). Sleep: Pathological aspects. Retrieved March 19, 2026, from https://www.britannica.com/science/sleep/Pathological-aspects
Fronda, G., Angioletti, L., & Balconi, M. (2024). EEG correlates of moral decision-making: Effect of choices and offers types. AJOB Neuroscience, 15(3), 191–205. https://doi.org/10.1080/21507740.2024.2306270
General Sleep Corp. (n.d.). Zmachine Insight+: Clinical-grade sleep EEG monitor. Retrieved March 19, 2026, from https://www.generalsleep.com/zmachine-insight.html
Hestermann, E., Schreve, K., & Vandenheever, D. (2024). Enhancing deep sleep induction through a wireless in-ear EEG device delivering binaural beats and ASMR: A proof-of-concept study. Sensors, 24(23), Article 7471. https://doi.org/10.3390/s24237471
Holtze, B., Rosenkranz, M., Jaeger, M., Debener, S., & Mirkovic, B. (2022). Ear-EEG measures of auditory attention to continuous speech. Frontiers in Neuroscience, 16, Article 869424. https://doi.org/10.3389/fnins.2022.869426
Interaxon Inc. (n.d.). Muse 2: Brain sensing headband. Retrieved March 19, 2026, from https://choosemuse.com/products/muse-2
Joyner, M., Hsu, S. H., Martin, S., & Shafi, M. M. (2024). Using a standalone ear-EEG device for focal-onset seizure detection. Bioelectronic Medicine, 10, Article 4. https://doi.org/10.1186/s42234-023-00135-0
Ju, Y. S., Ooms, S. J., Sutphen, C., Macauley, S. L., Zangrilli, M. A., Jerome, G., & Holtzman, D. M. (2017). Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain, 140(8), 2104–2111. https://doi.org/10.1093/brain/awx148
Kaveh, R., Schwendeman, C., Pu, L., Ogbeide, O., Al-Aswad, L., & Arias, M. (2024). Wireless ear EEG to monitor drowsiness. Nature Communications, 15, Article 6520. https://doi.org/10.1038/s41467-024-48682-7
Kent, B. A., Casciola, A. A., Carlucci, S. K., Chen, M., Stager, S., Mirian, M. S., & Nygaard, H. B. (2022). Home EEG sleep assessment shows reduced slow-wave sleep in mild–moderate Alzheimer’s disease. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 8(1), Article e12347. https://doi.org/10.1002/trc2.12347
Khng, K. H., & Mane, R. (2020). Beyond BCI—Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school. Advanced Engineering Informatics, 45, Article 101106. https://doi.org/10.1016/j.aei.2020.101106
Lenartowicz, A., & Loo, S. K. (2014). Use of EEG to diagnose ADHD. Current Psychiatry Reports, 16(11), Article 498. https://doi.org/10.1007/s11920-014-0498-0
Li, H., & Wu, L. (2022). EEG classification of normal and alcoholic by deep learning. Brain Sciences, 12(6), Article 778. https://doi.org/10.3390/brainsci12060778
Lin, C. T., Chuang, C. H., Huang, C. S., Tsai, S. F., Lu, S. W., & Chen, Y. H. (2014). Wireless and wearable EEG system for evaluating driver vigilance. IEEE Transactions on Biomedical Circuits and Systems, 8(2), 165–176. https://doi.org/10.1109/TBCAS.2014.2316224
Manohara, N., Ferrari, A., Greenblatt, A., Berardino, A., Peixoto, C., Duarte, F., & Lobo, F. A. (2025). Electroencephalogram monitoring during anesthesia and critical care: A guide for the clinician. Journal of Clinical Monitoring and Computing, 39(2), 315–348. https://doi.org/10.1007/s10877-024-01250-2
Markov, K., Elgendi, M., & Menon, C. (2024). EEG-based headset sleep wearable devices. Communications Medicine, 4, Article 13. https://doi.org/10.1038/s44328-024-00013-y
Mihajlović, V., Grundlehner, B., Vullers, R., & Penders, J. (2015). Wearable, wireless EEG solutions in daily life applications: What are we missing? IEEE Journal of Biomedical and Health Informatics, 19(1), 6–21. https://doi.org/10.1109/JBHI.2014.2328317
Mussigmann, T., Bardel, B., Créange, A., Senova, S., Goujon, C., Gendre, T., & Lefaucheur, J. P. (2025). Relieving chronic neuropathic pain with EEG-neurofeedback. European Journal of Neurology, 32(9), Article e70363. https://doi.org/10.1111/ene.70363
Mussigmann, T., Bardel, B., & Lefaucheur, J. P. (2022). Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain: A systematic review. NeuroImage, 258, Article 119351. https://doi.org/10.1016/j.neuroimage.2022.119351
NeuroSky Inc. (n.d.). MindWave: Mobile brainwave starter kit. Retrieved March 19, 2026, from https://neurosky.com/neurosky-products/mindwave-headset/
Nielsen, J. M., Zibrandtsen, I. C., Masulli, P., Sørensen, T. L., Andersen, T. S., & Kjær, T. W. (2022). Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study. Clinical Neurophysiology, 136, 40–48. https://doi.org/10.1016/j.clinph.2022.01.005
Nir, Y., Massimini, M., Boly, M., & Tononi, G. (2013). Sleep and consciousness. In G. Tononi (Ed.), Neuroimaging of consciousness (pp. 133–182). Springer. https://doi.org/10.1007/978-3-642-37580-4_9
Niso, G., Romero, E., Moreau, J. T., Araujo, A., & Krol, L. R. (2023). Sleep stages and cycles [Figure]. In Wireless EEG: A survey of systems and studies. NeuroImage, 269, Article 119895. https://doi.org/10.1016/j.neuroimage.2022.119895
OpenBCI Inc. (n.d.). Cyton Board: 8-channel neural interface. Retrieved March 19, 2026, from https://docs.openbci.com/GettingStarted/Boards/CytonGS/
Paruthi, S., Brooks, L. J., D’Ambrosio, C., Hall, W. A., Kotagal, S., Lloyd, R. M., & Wise, M. S. (2016). Consensus statement of the American Academy of Sleep Medicine on the recommended amount of sleep for healthy children: Methodology and discussion. Journal of Clinical Sleep Medicine, 12(11), 1549–1561. https://doi.org/10.5664/jcsm.6288
Sabio, J., Williams, N. S., McArthur, G. M., & Badcock, N. A. (2023). A scoping review on the use of consumer-grade EEG devices for research. PLOS ONE, 18(9), Article e0291186. https://doi.org/10.1371/journal.pone.0291186
Sakib, N., Islam, M. K., & Faruk, T. (2023). Machine learning model for computer-aided depression screening among young adults using wireless EEG headset. Computational Intelligence and Neuroscience, 2023, Article 1701429. https://doi.org/10.1155/2023/1701429
Shuffrey, L. C., Pini, N., Mei, H., Rodriguez, C., Gimenez, L. A., Barbosa, J. R., & Fifer, W. P. (2022). Maternal gestational diabetes mellitus (GDM) moderates the association between birth weight and (EEG) power in healthy term-age newborns. Developmental Psychobiology, 64(4), Article e22267. https://doi.org/10.1002/dev.70014
Shyaa, N. S. (2018). Electroencephalography (EEG) based mobile robot control through an adaptive brain robot interface. American Scientific Research Journal for Engineering, Technology, and Sciences, 42(1), 139–147.
Vincenzo, R., Marianna, C., Rossella, C., & Giulia, C. (2026). Beyond the lab: Real-world benchmarking of wearable EEGs for passive brain-computer interfaces. Brain Informatics, 13, Article 3. https://doi.org/10.1186/s40708-025-00290-x
Welte, T. M., Janner, F., Lindner, S., Gollwitzer, S., Stritzelberger, J., Lang, J. D., & Hamer, H. M. (2024). Evaluation of simplified wireless EEG recordings in the neurological emergency room. PLOS ONE, 19(10), Article e0310223. https://doi.org/10.1371/journal.pone.0310223
Yuan, I., Bong, C. L., & Chao, J. Y. (2024). Intraoperative pediatric electroencephalography monitoring: An updated review. Korean Journal of Anesthesiology, 77(3), 289–305. https://doi.org/10.4097/kja.23843
Zamm, A., Palmer, C., Bauer, A. K. R., Bleichner, M. G., Demos, A. P., & Debener, S. (2019). Synchronizing MIDI and wireless EEG measurements during natural piano performance. Brain Research, 1716, 27–38. https://doi.org/10.1016/j.brainres.2017.07.001
Zeydabadinezhad, M., Jowers, J., Buhl, D., Cabaniss, B., & Mahmoudi, B. (2024). A personalized earbud for noninvasive long-term EEG monitoring. Journal of Neural Engineering, 21(2), Article 026017. https://doi.org/10.1088/1741-2552/ad33af
Zhang, R., Tomasi, D., Manza, P., & Volkow, N. D. (2021). Sleep disturbances are associated with cortical and subcortical atrophy in alcohol use disorder. Translational Psychiatry, 11, Article 428. https://doi.org/10.1038/s41398-021-01534-0
Zou, J., Chen, H., Chen, X., Lin, Z., Yang, Q., Tie, C., & Zheng, H. (2024). Noninvasive closed-loop acoustic brain-computer interface for seizure control. Theranostics, 14(15), 5965–5981. https://doi.org/10.7150/thno.99820
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Copyright (c) 2026 Katarzyna Anna Borzęcka, Agnieszka Szwed, Aleksandra Sołtys, Daria Aleksandra Warzocha-Żurek , Ewa Maria Polewczak-Karp, Katarzyna Wawrzonek , Krystian Andryszko, Marcelina Dymon, Natalia Matylda Ziemba-Furgała, Paulina Krysa
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