Revisión de electroencefalografía portable y su aplicabilidad en neurociencias.
DOI:
https://doi.org/10.33571/rpolitec.v17n34a9Palabras clave:
EEG móvil, EEG portable, Interfaz cerebro-computador, Señal EEGResumen
La electroencefalografía (EEG) es una técnica que permite registrar la actividad eléctrica del cerebro y ha sido estudiada durante los últimos cien años en diferentes ámbitos de la neurociencia. En los últimos años se ha investigado y desarrollado equipos de medición que sean portables y que permitan una buena calidad de la señal, por lo cual se realizó una revisión bibliográfica de las compañías fabricantes de algunos dispositivos de electroencefalografía portable disponibles en el mercado, se exponen sus características principales, algunos trabajos encontrados que fueron realizados con los dispositivos, comparaciones entre los mismos y una discusión acerca de las ventajas y desventajas de sus características. Finalmente se concluye que a la hora de comprar un dispositivo para electroencefalografía portable es necesario tener en cuenta el uso que se le va a dar y el costo-beneficio que tiene el equipo de acuerdo con sus características.
Encephalography is a technique that allows the recording of electrical activity of the brain and has been studied during the last hundred years in different areas of neuroscience. For several years, measuring equipment that are portable and that allow a good signal quality to have been researched and developed, so a literature review of the manufacturing companies of some of portable electroencephalography devices available on the market was carried out: Its main features are exposed, as well as some of the work found that were made with those, comparisons between them and a discussion about the advantages and disadvantages of their features. It is concluded that, when a portable encephalography device is bought, it’s necessary to take into consideration the use that it will be having and the cost-benefit that the device has according to its features.
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B. Hans, “Uber das elektrenkephalogramm des menshen,” Arch. für Psychiatr. Nervenkrankheiten, vol. 278, no. 1875, pp. 87: 527-570., 1929.
F. Ramos-Argüelles, G. Morales, S. Egozcue, R. M. Pabón, and M. T. Alonso, “Técnicas básicas de electroencefalografía: principios y aplicaciones clínicas.,” An. Sist. Sanit. Navar., vol. 32 Suppl 3, pp. 69–82, 2009.
J. J. Vidal, “Toward Direct Brain-Computer Communication,” Annual Review of Biophysics and Bioengineering, vol. 2, no. 1. pp. 157–180, 1973.
F. Lopes da Silva, “EEG and MEG: Relevance to Neuroscience,” Neuron, vol. 80, no. 5, pp. 1112–1128, Dec. 2013.
J. Tohka and U. Ruotsalainen, “Imaging brain change across different time scales.,” Front. Neuroinform., vol. 6, p. 29, Jan. 2012.
X. Zhang, X. Lei, T. Wu, and T. Jiang, “A review of EEG and MEG for brainnetome research.,” Cogn. Neurodyn., vol. 8, no. 2, pp. 87–98, Apr. 2014.
D. J. A. Smit et al., “Endophenotypes in a dynamically connected brain.,” Behav. Genet., vol. 40, no. 2, pp. 167–77, Mar. 2010.
P. M. Rossini, S. Rossi, C. Babiloni, and J. Polich, “Clinical neurophysiology of aging brain: From normal aging to neurodegeneration,” Prog. Neurobiol., vol. 83, no. 6, pp. 375–400, Dec. 2007.
C. Babiloni et al., “Sources of cortical rhythms change as a function of cognitive impairment in pathological aging: a multicenter study,” Clin. Neurophysiol., vol. 117, no. 2, pp. 252–268, Feb. 2006.
E. Gallego-Jutgla et al., “Diagnosis of Alzheimer’s disease from EEG by means of synchrony measures in optimized frequency bands.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2012, pp. 4266–70, Aug. 2012.
C. Babiloni et al., “Classification of Single Normal and Alzheimer’s Disease Individuals from Cortical Sources of Resting State EEG Rhythms,” Front. Neurosci., vol. 10, p. 47, Feb. 2016.
S. Nobukawa, T. Yamanishi, S. Kasakawa, H. Nishimura, M. Kikuchi, and T. Takahashi, “Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease,” Front. Psychiatry, vol. 11, Apr. 2020.
J. Carmona, J. Suarez, and J. F. Ochoa Gomez, “Brain functional connectivity in Parkinson’s disease – EEG resting analysis,” in IFMBE Proceedings, 2017, vol. 60, pp. 185–188.
J.-M. Melgari et al., “Alpha and beta EEG power reflects L-dopa acute administration in parkinsonian patients,” Front. Aging Neurosci., vol. 6, p. 302, 2014.
J. F. Ochoa et al., “Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer’s Disease Detected Using Quantitative Electroencephalography.,” J. Alzheimers. Dis., pp. 1–16, May 2017.
M. Kreuzer, “EEG based monitoring of general anesthesia: Taking the next steps,” Front. Comput. Neurosci., vol. 11, Jun. 2017.
A. Lenartowicz and S. K. Loo, “Use of EEG to Diagnose ADHD,” Current Psychiatry Reports, vol. 16, no. 11. Current Medicine Group LLC 1, p. 498, 2014.
J. J. Vidal, “Cyberspace Bionics,” 1999, pp. 203–218.
J. Minguillon, M. A. Lopez-Gordo, and F. Pelayo, “Trends in EEG-BCI for daily-life: Requirements for artifact removal,” Biomed. Signal Process. Control, vol. 31, pp. 407–418, 2017.
J. L. Park, M. M. Fairweather, and D. I. Donaldson, “Making the case for mobile cognition: EEG and sports performance,” Neurosci. Biobehav. Rev., vol. 52, no. July 2016, pp. 117–130, 2015.
J. Xu and B. Zhong, “Review on portable EEG technology in educational research,” Comput. Human Behav., vol. 81, pp. 340–349, 2018.
T. Neumann et al., “Diagnostic and therapeutic yield of a patient-controlled portable EEG device with dry electrodes for home-monitoring neurological outpatients-rationale and protocol of the HOMEONE pilot study,” Pilot Feasibility Stud., vol. 4, no. 1, Apr. 2018.
T. Neumann et al., “Assessment of the technical usability and efficacy of a new portable dry-electrode EEG recorder: First results of the HOMEONE study,” Clin. Neurophysiol., vol. 130, no. 11, pp. 2076–2087, Nov. 2019.
V. Mihajlovic, B. Grundlehner, R. Vullers, and J. Penders, “Wearable, wireless EEG solutions in daily life applications: What are we missing?,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 1, pp. 6–21, 2015.
J. A. Martinez-Leon, J. M. Cano-Izquierdo, and J. Ibarrola, “Are low cost Brain Computer Interface headsets ready for motor imagery applications?,” Expert Syst. Appl., vol. 49, pp. 136–144, 2016.
R. A. Ramadan and A. V Vasilakos, “Brain Computer Interface: Control Signals Review,” Neurocomputing, vol. 223, no. October 2016, pp. 1–19, 2016.
O. E. Krigolson, C. C. Williams, A. Norton, C. D. Hassall, and F. L. Colino, “Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research,” Front. Neurosci., vol. 11, no. MAR, pp. 1–10, 2017.
EMOTIV Inc, “Homepage - Emotiv,” 2018.
S. Katsigiannis and N. Ramzan, “DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 1, pp. 98–107, 2018.
J. Loviscach, “Chapter 3 Playing with All Senses. Human-Computer Interface Devices for Games,” Adv. Comput., vol. 77, no. 09, pp. 79–115, 2009.
B. Nakisa, M. N. Rastgoo, D. Tjondronegoro, and V. Chandran, “Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors,” Expert Syst. Appl., vol. 93, pp. 143–155, 2018.
J. I. Ekandem, T. A. Davis, I. Alvarez, M. T. James, and J. E. Gilbert, “Evaluating the ergonomics of BCI devices for research and experimentation,” Ergonomics, vol. 55, no. 5, pp. 592–598, 2012.
H. Ekanayake, “P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG?,” Web Publ. http//neurofeedback. visaduma. info/ …, p. 16, 2010.
S. Schiff et al., “A low-cost, user-friendly electroencephalographic recording system for the assessment of hepatic encephalopathy,” Hepatology, vol. 63, no. 5, pp. 1651–1659, 2016.
Q. Wang and O. Sourina, “Real-time mental arithmetic task recognition from EEG signals,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 21, no. 2, pp. 225–232, 2013.
M. De Vos, M. Kroesen, R. Emkes, and S. Debener, “P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.,” J. Neural Eng., vol. 11, no. 3, p. 036008, 2014.
EASYCAP, “Home - EASYCAP | EEG Recording Caps and Related Products,” 2018.
S. Debener, F. Minow, R. Emkes, K. Gandras, and M. de Vos, “How about taking a low-cost, small, and wireless EEG for a walk?,” Psychophysiology, vol. 49, no. 11, pp. 1617–1621, 2012.
M. De Vos, K. Gandras, and S. Debener, “Towards a truly mobile auditory brain-computer interface: Exploring the P300 to take away,” Int. J. Psychophysiol., vol. 91, no. 1, pp. 46–53, 2014.
mBrainTrain - mbt, “mBrainTrain | Smarting,” 2016.
M. G. Bleichner and S. Debener, “Concealed, unobtrusive ear-centered EEG acquisition: Ceegrids for transparent EEG,” Front. Hum. Neurosci., vol. 11, no. April, pp. 1–14, 2017.
NeuroSky; Sitemap, “EEG - ECG - Biosensors,” 2018.
NeuroSky, “A Real Game-Changer: BCI & EEG Use Cases for Video Games,” 2015.
F. Liarokapis, K. Debattista, A. Vourvopoulos, P. Petridis, and A. Ene, “Comparing interaction techniques for serious games through brain – computer interfaces: A user perception evaluation study q,” Entertain. Comput., vol. 5, no. 4, pp. 391–399, 2014.
Y. Ci and S. Wang, “The key techniques research on portable EEG examination expert system,” 10th Int. Conf. Comput. Sci. Educ. ICCSE 2015. Fitzwilliam Coll. Cambridge Univ. UK, no. Iccse, pp. 975–978, 2015.
J. M. Rogers, S. J. Johnstone, A. Aminov, J. Donnelly, and P. H. Wilson, “Test-retest reliability of a single-channel, wireless EEG system,” Int. J. Psychophysiol., vol. 106, pp. 87–96, 2016.
A. Vourvopoulos and F. Liarokapis, “Evaluation of commercial brain–computer interfaces in real and virtual world environment: A pilot study,” Comput. Electr. Eng., vol. 40, no. 2, pp. 714–729, 2014.
M. Abo-Zahhad, S. M. Ahmed, and S. N. Abbas, “A new multi-level approach to EEG based human authentication using eye blinking,” Pattern Recognit. Lett., vol. 82, pp. 216–225, 2016.
K. S. Hemington and J. N. Reynolds, “Electroencephalographic correlates of working memory deficits in children with Fetal Alcohol Spectrum Disorder using a single-electrode pair recording device,” Clin. Neurophysiol., vol. 125, no. 12, pp. 2364–2371, 2014.
g.tec, “g.tec medical engineering,” 2018.
L. Seungchan, S. Younghak, W. Soogil, K. Kiseon, and L. Heung-No, “Chapter 11. Review of Wireless Brain-Computer Interface Systems,” in Brain-Computer Interface Systems - Recent Progress and Future Prospects, Reza Fazel., INTECH, 2013, pp. 215–238.
S. G. Hajra et al., “Developing brain vital signs: Initial framework for monitoring brain function changes over time,” Front. Neurosci., vol. 10, no. MAY, pp. 1–10, 2016.
Advanced Brain Monitoring Inc, “Advanced Brain Monitoring - Neurotechnology & Advanced Sleep,” 2015.
T. Radüntz and B. Meffert, “User experience of 7 mobile electroencephalography devices: Comparative study,” JMIR mHealth uHealth, vol. 7, no. 9, 2019.
Quasar, “Quasar USA,” 2016.
Wearable Sensing, “Wireless Dry Electrode EEG Systems for Neuroscience Research,” 2013.
I. Fridman et al., “Evaluation of Dry Sensors for Neonatal EEG Recordings,” J. Clin. Neurophysiol., vol. 33, no. 2, pp. 149–155, 2016.
Neuroelectrics, “Products / ENOBIO - Neuroelectrics,” 2018.
E. Ratti, S. Waninger, C. Berka, G. Ruffini, and A. Verma, “Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials,” Front. Hum. Neurosci., vol. 11, no. August, pp. 1–7, 2017.
L. Billeci et al., “An integrated approach for the monitoring of brain and autonomic response of children with Autism Spectrum Disorders during treatment by wearable technologies,” Front. Neurosci., vol. 10, no. JUN, 2016.
ANT Neuro, “eegoTMsports | ANT Neuro,” 2018.
EGI, “Ultra-Mobile Eeg & Emg Recording Platform,” Enschede, The Netherlands.
M. Duvinage, T. Castermans, M. Petieau, T. Hoellinger, G. Cheron, and T. Dutoit, “Performance of the Emotiv Epoc headset for P300-based applications,” Biomed. Eng. Online, vol. 12, no. 1, p. 56, 2013.
F. Zinke, A. Gebel, U. Granacher, and O. Prieske, “Acute effects of short-term local tendon vibration on plantar flexor torque, muscle contractile properties, neuromuscular and brain activity in young athletes,” J. Sport. Sci. Med., vol. 18, no. 2, pp. 327–336, 2019.
Mitsar Co. Ltd, “Mitsar-EEG systems: EEG equipment for routine EEG, qEEG, ERP and video EEG studies,” 2018.
MITSAR, “Electroencephalography System,” Saint Petersburg.
R. Ikramov et al., “Initial evaluation of the brain activity under different software development situations,” Proc. Int. Conf. Softw. Eng. Knowl. Eng. SEKE, vol. 2019-July, no. July, pp. 741–747, 2019.
I. Genuth, “All In The Mind,” Eng. Technol., vol. 10, no. June, pp. 37–39, 2015.
OpenBCI, “OpenBCI - Open Source Biosensing Tools (EEG, EMG, EKG, and more),” 2017.
J. M. Qiu, M. A. Casey, and S. G. Diamond, “Assessing Feedback Response With a Wearable Electroencephalography System,” Front. Hum. Neurosci., vol. 13, no. July, pp. 1–14, 2019.
N. Kaongoen and S. Jo, “A novel hybrid auditory BCI paradigm combining ASSR and P300,” J. Neurosci. Methods, vol. 279, pp. 44–51, 2017.
“myBrain Technologies,” 2018.
“MUSE TM | Meditation Made Easy,” 2017.
Electrical Geodesics Inc, “Wireless, portable Avatar recorder for EEG, ECG, EOG, and EMG,” 2018.
K. Anastasia and T. Louise, “Architecture and Neuroscience; what can the EEG recording of brain activity reveal about a walk through everyday spaces?,” Int. J. Parallel, Emergent Distrib. Syst., vol. 5760, pp. 1–16, 2018.
H. Xiao, Y. Duan, Z. Zhang, and M. Li, “Detection and estimation of mental fatigue in manual assembly process of complex products,” Assem. Autom., p. AA-03-2017-040, 2017.
A. Rodríguez, B. Rey, M. Clemente, M. Wrzesien, and M. Alcañiz, “Assessing brain activations associated with emotional regulation during virtual reality mood induction procedures,” Expert Syst. Appl., vol. 42, no. 3, pp. 1699–1709, 2015.
S. Fok et al., “An EEG-Based Brain Computer Interface for Rehabilitation and Restoration of Hand Control Following Stroke Using Ipsilateral Cortical Physiology,” in Proceedings of the 33 annual international conference of the IEEE on engineering in medicine and biology society, EMBC,Boston, USA, 2011, pp. 6277–6280.
M. Ben Dkhil, M. Neji, A. Wali, and A. M. Alimi, “A new approach for a safe car assistance system,” in 20154th IEEE International Conference on Advanced logistics and Transport (ICAl T), 2015, pp. 217–222.
T. Mcmahan, I. Parberry, and T. D. Parsons, “Modality Specific Assessment of Video Game Player’s Experience Using the Emotiv,” Entertain. Comput., vol. 7, pp. 1–6, 2015.
C. P. Amaral, M. A. Simões, S. Mouga, J. Andrade, and M. Castelo-branco, “A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study,” vol. 290, pp. 105–115, 2017.
L.-D. Liao et al., “Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors,” J. Neuroeng. Rehabil., vol. 9, no. 1, p. 5, 2012.
O. R. Daniela, H. I. Verónica and O. G. John, "SSVEP Study in Monocular and Binocular Vision," 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), 2019, pp. 1-5, doi: 10.1109/STSIVA.2019.8730241.
V. Gaviria García et al., "Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography", Colombian Journal of Anesthesiology, vol. 49, no. 2, 2020. Available: 10.5554/22562087.e956
J. Salgado Patrón and C. Barrera Monje, "Emotiv EPOC BCI with Python on a Raspberry pi", Sistemas y Telemática, vol. 14, no. 36, pp. 27-38, 2016. Available: 10.18046/syt.v14i36.2217
K. Correa Arana and O. Vivas Albán, "Prótesis de Mano Virtual Movida Por Señales Encefalograficas – EEG.", Prospectiva, vol. 14, no. 2, p. 99, 2016. Available: 10.15665/rp.v14i2.664