Electroencephalography (EEG) Technology Applications and Available Devices
Source abstract
Abstract
The electroencephalography (EEG) sensor has become a prominent sensor in the study of brain activity. Its applications extend from research studies to medical applications. This review paper explores various types of EEG sensors and their applications. This paper is for an audience that comprises engineers, scientists and clinicians who are interested in learning more about the EEG sensors, the various types, their applications and which EEG sensor would suit a specific task. The paper also lists the details of each of the sensors currently available in the market, their technical specs, battery life, and where they have been used and what their limitations are.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Neural dynamics and brain function · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, University of Minnesota Duluth, Duluth, MN 55812, USA
References (93)
Show source-provided references
1. Sullivan, T.J., Deiss, S.R., and Cauwenberghs, G. (2007, January 27–30). A low-noise, non-contact EEG/ECG sensor. Proceedings of the 2007 IEEE Biomedical Circuits and Systems Conference, Montreal, QC, Canada.10.1109/BIOCAS.2007.4463332 ↗
2. Bronzino, J.D. (1999). Biomedical Engineering Handbook, CRC Press.10.1201/9781003040682 ↗
3. Systematic comparison between a wireless EEG system with dry electrodes and a wired EEG system with wet electrodes10.1016/j.neuroimage.2018.09.012 ↗
4. Bastos, T., Müller, S., Godinez Tello, R., and Ferreira, A. (2014, January 26–30). Comparison between wire and wireless EEG acquisition systems based on SSVEP in an Independent-BCI. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA.
5. Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications10.1038/s41598-020-62154-0 ↗
6. Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T., and Lin, C. (2020). EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications. arXiv.10.1109/TCBB.2021.3052811 ↗
7. American Clinical Neurophysiology Society Guideline 2: Guidelines for Standard Electrode Position Nomenclature10.1097/WNP.0000000000000316 ↗
8. The five percent electrode system for high-resolution EEG and ERP measurements10.1016/S1388-2457(00)00527-7 ↗
9. Abhang, P.A., Gawali, B.W., and Mehrotra, S.C. (2016). Introduction to EEG- and Speech-Based Emotion Recognition, Academic Press Inc.. [1st ed.].10.1016/B978-0-12-804490-2.00007-5 ↗
10. BCI2000: A general-purpose brain-computer interface (BCI) system10.1109/TBME.2004.827072 ↗
11. Guger, C., Allison, B., and Edlinger, G. (2013). Brain-Computer Interface Research: A State-Of-The-Art Summary, Springer.10.1007/978-3-642-36083-1 ↗
12. Guger, C., Allison, B., and Leuthardt, E.C. (2014). Brain-Computer Interface Research: A State-Of-The-Art Summary 2, Springer.10.1007/978-3-642-54707-2 ↗
13. Guger, C., Allison, B., and Leuthardt, E.C. (2014). Brain-Computer Interface Research: A State-Of-The-Art Summary 3, Springer.10.1007/978-3-642-54707-2 ↗
14. Guger, C., Muller-Putz, G., and Allison, B. (2015). Brain-Computer Interface Research: A State-Of-The-Art Summary 4, Springer.10.1007/978-3-319-25190-5 ↗
15. Guger, C., Allison, B., and Ushiba, J. (2017). Brain-Computer Interface Research: A State-Of-The-Art Summary 5, Springer.10.1007/978-3-319-57132-4 ↗
16. Guger, A., Lebedev, G., Christoph, A., and Lebedev, M. (2017). Brain-Computer Interface Research: A State-Of-The-Art Summary 6, Springer.10.1007/978-3-319-64373-1 ↗
17. Guger, C., Mrachacz-Kersting, N., and Allison, B.Z. (2019). Brain-Computer Interface Research: A State-Of-The-Art Summary 7, Springer.10.1007/978-3-030-05668-1 ↗
18. Kline, A., and Desai, J. (2014, January 25–27). SIMULINK® based robotic hand control using Emotiv™ EEG headset. Proceedings of the 2014 40th Annual Northeast Bioengineering Conference (NEBEC), Boston, MA, USA.
19. Ashford, R. (2015). ThinkerBelle EEG Amplifying Dress, Association for Computing Machinery.10.1145/2800835.2801673 ↗
20. Ranky, G.N., and Adamovich, S. (2010, January 26–28). Analysis of a commercial EEG device for the control of a robot arm. Proceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference (NEBEC), New York, NY, USA.10.1109/NEBC.2010.5458188 ↗
21. Towards an EEG-based brain-computer interface for online robot control10.1007/s11042-015-2717-z ↗
22. Buck, T., Matthews, A., and Alba-Flores, R. (2019). Robotic Arm Control through the Use of Human Machine Interfaces and Brain Signals, SoutheastCon.10.1109/SoutheastCon42311.2019.9020526 ↗
23. Chowdhury, P., Shakim, S.S.K., Karim, M.R., and Rhaman, M.K. (2014, January 23–24). Cognitive efficiency in robot control by Emotiv EPOC. Proceedings of the 2014 International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, Bangladesh.10.1109/ICIEV.2014.6850775 ↗
24. Grude, S., Freeland, M., Yang, C., and Ma, H. (2013, January 26–28). Controlling mobile Spykee robot using Emotiv Neuro headset. Proceedings of the 32nd Chinese Control Conference, Xi’an, China.
25. Perera, C.J., Naotunna, I., Sadaruwan, C., Gopura, R.A.R.C., and Lalitharatne, T.D. (2016, January 9–12). SSVEP based BMI for a meal assistance robot. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.10.1109/SMC.2016.7844580 ↗
26. Katona, J., Ujbanyi, T., Sziladi, G., and Kovari, A. (2016, January 16–18). 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.10.1109/CogInfoCom.2016.7804557 ↗
27. Pan, P., Tan, G., and Phyo Wai, A.A. (2020, August 18). Evaluation of Consumer-Grade EEG Headsets for BCI Drone Control. Available online: http://oar.a-star.edu.sg:80/jspui/handle/123456789/2149.
28. Controlling in-vehicle systems with a commercial EEG headset: Performance and cognitive load
29. Poorna, S.S., Baba, P.S., Ramya, G.L., Poreddy, P., Aashritha, L.S., Nair, G.J., and Renjith, S. (2016, January 15–17). Classification of EEG based control using ANN and KNN—A comparison. Proceedings of the 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India.10.1109/ICCIC.2016.7919524 ↗
30. Lee, S., Cho, H., Yoon, K.J., and Lee, J. (2013). Semi-autonomous Car Control Using Brain Computer Interfaces. Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, Springer.
31. Shivappa, V.K.K., Luu, B., Solis, M., and George, K. (2018, January 14–17). Home automation system using brain computer interface paradigm based on auditory selection attention. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.10.1109/I2MTC.2018.8409863 ↗
32. Campbell, A., Choudhury, T., Hu, S., Lu, H., Mukerjee, M., Rabbi, M., and Raizada, R. (September, January 30). NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset. Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems and Applications on Mobile Handhelds, New Delhi, India.
33. Mistry, K.S., Pelayo, P., Anil, D.G., and George, K. (2018, January 14–17). An SSVEP based brain computer interface system to control electric wheelchairs. Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.10.1109/I2MTC.2018.8409632 ↗
34. Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device10.24003/emitter.v5i1.165 ↗
35. Carrino, F., Dumoulin, J., Mugellini, E., Khaled, O.A., and Ingold, R. (2012, January 9). A self-paced BCI system to control an electric wheelchair: Evaluation of a commercial, low-cost EEG device. Proceedings of the 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), Manaus, Brazil.10.1109/BRC.2012.6222185 ↗
36. Mirza, I.A., Tripathy, A., Chopra, S., D’Sa, M., Rajagopalan, K., D’Souza, A., and Sharma, N. (2015, January 4–6). Mind-controlled wheelchair using an EEG headset and arduino microcontroller. Proceedings of the 2015 International Conference on Technologies for Sustainable Development (ICTSD), Mumbai, India.10.1109/ICTSD.2015.7095887 ↗
37. User-Friendly LabVIEW GUI for Prosthetic Hand Control Using Emotiv EEG Headset10.1016/j.procs.2017.01.222 ↗
38. Beyrouthy, T., al Kork, S.K., Korbane, J.A., and Abdulmonem, A. (2016, January 1–6). EEG Mind controlled Smart Prosthetic Arm. Proceedings of the 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech), Balaclava, Mauritius.10.1109/EmergiTech.2016.7737375 ↗
39. Spicer, R., Anglin, J., Krum, D.M., and Liew, S. (2017, January 18–22). REINVENT: A low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. Proceedings of the 2017 IEEE Virtual Reality (VR), Los Angeles, CA, USA.10.1109/VR.2017.7892338 ↗
40. EEG-NeuroBioFeedback Treatment of Patients with Brain Injury: Part 2: Changes in EEG Parameters versus Rehabilitation10.1300/J184v05n04_04 ↗
41. A Post-Stroke Rehabilitation System Integrating Robotics, VR and High-Resolution EEG Imaging10.1109/TNSRE.2013.2267851 ↗
42. Jang, W.A., Lee, S.M., and Lee, D.H. (2014, January 17–19). Development BCI for individuals with severely disability using EMOTIV EEG headset and robot. Proceedings of the 2014 International Winter Workshop on Brain-Computer Interface (BCI), Jeongsun-kun, Korea.10.1109/iww-BCI.2014.6782576 ↗
43. Jijun, T., Peng, Z., Ran, X., and Lei, D. (2015, January 25–29). The portable P300 dialing system based on tablet and Emotiv Epoc headset. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.10.1109/EMBC.2015.7318425 ↗
44. Jacoby, J.D., Tory, M., and Tanaka, J. (2015, January 24–26). Evoked response potential training on a consumer EEG headset. Proceedings of the 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Victoria, BC, USA.10.1109/PACRIM.2015.7334885 ↗
45. EEG Mouse: A Machine Learning-Based Brain Computer Interface
46. Asynchronous gaze-independent event-related potential-based brain-computer interface10.1016/j.artmed.2013.07.006 ↗
47. Hawsawi, O., and Semwal, S.K. (2014, January 5–8). EEG headset supporting mobility impaired gamers with game accessibility. Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA.10.1109/SMC.2014.6974015 ↗
48. Automated EEG analysis of epilepsy: A review10.1016/j.knosys.2013.02.014 ↗
49. Quantitative EEG as a predictive biomarker for Parkinson disease dementia10.1212/WNL.0b013e318224af8d ↗
50. A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements10.1016/j.artmed.2004.07.003 ↗
51. Trait and State EEG Indices of Information Processing in Developmental Dyslexia10.1016/S0167-8760(00)00075-1 ↗
52. ADHD and EEG-neurofeedback: A double-blind randomized placebo-controlled feasibility study10.1007/s00702-010-0524-2 ↗
53. Qualitative and quantitative EEG findings in schizophrenia10.1093/schbul/3.1.61 ↗
54. Alturki, F., Alsharabi, K., Abdurraqeeb, A., and Aljalal, M. (2020). EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques. Sensors, 20.10.3390/s20092505 ↗
55. The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data10.1016/j.compbiomed.2020.103722 ↗
56. Cyclic alternating pattern (CAP): The marker of sleep instability10.1016/j.smrv.2011.02.003 ↗
57. Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC10.1016/j.slsci.2016.07.002 ↗
58. Wang, Y. (2020). EEG-based Anxious Personality Prediction. [Ph.D. Thesis, University of Otago]. Available online: http://hdl.handle.net/10523/10044.
59. Comparative EEG mapping studies in Huntington’s disease patients and controls10.1007/s00702-010-0491-7 ↗
60. Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task10.1016/j.compbiomed.2019.103596 ↗
61. Maruyama, Y., Yoshimura, N., Rana, A., Malekshahi, A., Tonin, A., Jaramillo-Gonzalez, A., Birbaumer, N., and Chaudhary, U. (2020). Electroencephalography of completely locked-in state patients with amyotrophic lateral sclerosis. Neurosci Res.10.1016/j.neures.2020.01.013 ↗
62. Traumatic brain injury: An EEG point of view10.1590/1980-57642016dn11-010002 ↗
63. Bryan The EEG in Coma10.1097/00004691-200009000-00006 ↗
64. EEG, Consciousness, and Sleep10.1126/science.124.3231.1066 ↗
65. Classification of the EEG during neurosurgery. Parametric identification and Kalman filtering compared10.1016/0141-5425(86)90091-9 ↗
66. Das, R., Chatterjee, D., Das, D., Sinharay, A., and Sinha, A. (2014, January 24–27). Cognitive load measurement—A methodology to compare low cost commercial EEG devices. Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, India.10.1109/ICACCI.2014.6968528 ↗
67. Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A., and D’Mello, S. (2017). Put Your Thinking Cap on: Detecting Cognitive Load Using EEG during Learning, Association for Computing Machinery.10.1145/3027385.3027431 ↗
68. Abnormality of EEG alpha asymmetry in female adolescent suicide attempters10.1016/0006-3223(95)00493-9 ↗
69. Cernea, D., Kerren, A., and Ebert, A. (2011, January 17–18). Detecting Insight and Emotion in Visualization Applications with a Commercial EEG Headset. Proceedings of SIGRAD 2011, Evaluations of Graphics and Visualization—Efficiency, Usefulness, Accessibility, Usability, Stockholm, Sweden.
70. Frontal EEG theta changes assess the training improvements of novices in flight simulation tasks
71. Sun, S. (2008, January 8–11). Multitask learning for EEG-based biometrics. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.
72. EEG sleep patterns in mental retardation10.1016/0013-4694(69)90165-5 ↗
73. Effects of light intervention on alertness and mental performance during the post-lunch dip: A multi-measure study10.2486/indhealth.2018-0030 ↗
74. Wu, E.Q., Deng, P.Y., Qu, X.Y., Tang, Z., Zhang, W.M., Zhu, L.M., Ren, H., Zhou, G.R., and Sheng, R.S. (2020). Detecting Fatigue Status of Pilots Based on Deep Learning Network Using EEG Signals. IEEE Trans. Cogn. Dev. Syst.10.1109/TCDS.2019.2963476 ↗
75. Mental workload estimations in unilateral deafened children
76. Duru, D.G., Duru, A.D., Barkana, D.E., Sanli, O., and Ozkan, M. (2013, January 6–8). Assessment of surgeon’s stress level and alertness using EEG during laparoscopic simple nephrectomy. Proceedings of the 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA.10.1109/NER.2013.6695969 ↗
77. Kalas, M.S., and Momin, B.F. (2016, January 3–5). Stress detection and reduction using EEG signals. Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India.10.1109/ICEEOT.2016.7755604 ↗
78. Sitting or Walking? Analyzing the Neural Emotional Indicators of Urban Green Space Behavior with Mobile EEG10.1007/s11524-019-00407-8 ↗
79. Empirical wavelet transform based automated alcoholism detecting using EEG signal features10.1016/j.bspc.2019.101777 ↗
80. Liu, Y., Sourina, O., and Nguyen, M.K. (2010, January 20–22). Real-Time EEG-Based Human Emotion Recognition and Visualization. Proceedings of the 2010 International Conference on Cyberworlds, Singapore.10.1109/CW.2010.37 ↗
81. Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking10.1016/j.eswa.2012.12.095 ↗
82. Being present in a real or virtual world: A EEG study10.1016/j.ijmedinf.2019.103977 ↗
83. Vijayaragavan, G.R., Raghav, R.L., Phani, K.P., and Vaidyanathan, V. (2015, January 8–10). EEG monitored mind de-stressing smart phone application using Yoga and Music Therapy. Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, India.10.1109/ICGCIoT.2015.7380498 ↗
84. Biswas, G., Bull, S., Kay, J., and Mitrovic, A. (2011). Toward Exploiting EEG Input in a Reading Tutor. Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, Springer.10.1007/978-3-642-21869-9 ↗
85. Hsu, L., and Chen, Y. (2020). Neuromarketing, subliminal advertising, and hotel selection: An EEG study. Australas. Mark. J.10.1016/j.ausmj.2020.04.009 ↗
86. Soria Morillo, L., Alvarez-Garcia, J., Gonzalez-Abril, L., and Ortega, J. (2015). Advertising Liking Recognition Technique Applied to Neuromarketing by Using Low-Cost EEG Headset, Springer.10.1007/978-3-319-16480-9_68 ↗
87. Neuromarketing for a better understanding of consumer needs and emotions10.14807/ijmp.v11i1.993 ↗
88. Li, S., Cha, S., and Tappert, C.C. (2018, January 22–25). Biometric Distinctiveness of Brain Signals Based on EEG. Proceedings of the 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), Redondo Beach, CA, USA.10.1109/BTAS.2018.8698540 ↗
89. la Rocca, D., Campisi, P., and Scarano, G. (2012, January 6–7). EEG biometrics for individual recognition in resting state with closed eyes. Proceedings of the 2012 BIOSIG—The International Conference of Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.
90. Wang, H., Li, Y., Hu, X., Yang, Y., Meng, Z., and Chang, K.-M. (2013, January 3–4). Using EEG to improve massive open online courses feedback interaction. Proceedings of the CEUR Workshop Proceedings, Rio de Janeiro, Brazil.
91. Cirett Galan, F., and Beal, C. (2012). EEG Estimates of Engagement and Cognitive Workload Predict Math Problem Solving Outcomes. User Modeling, Adaptation, and Personalization, Springer.10.1007/978-3-642-31454-4_5 ↗
92. Frey, J., Gervais, R., Lainé, T., Duluc, M., Germain, H., Fleck, S., Lotte, F., and Hachet, M. (2017). Scientific Outreach with Teegi, a Tangible EEG Interface to Talk about Neurotechnologies, Association for Computing Machinery.10.1145/3027063.3052971 ↗
93. Mercier-Ganady, J., Lotte, F., Loup-Escande, E., Marchal, M., and Lécuyer, A. (April, January 29). The Mind-Mirror: See Your Brain in Action in your Head Using EEG and Augmented Reality. Proceedings of the IEEE Virtual Reality, Minneapolis, MN, USA.