Design and Characterization of an EEG-Hat for Reliable EEG Measurements
Abstract
:1. Introduction
2. Design and Fabrication
2.1. Module Design
- simple hair avoidance
- adjustable contact force of the CMEs
- perpendicular contact between the CME pillars and the scalp
- sustained pressing of the CMEs.
2.2. Fabrication
3. Experimental Procedures
3.1. Quantification of Contact Force and Hair Separation
3.2. Determination of the Shutter Angle
3.3. Rotation Angle of the Ball Joint
3.4. EEG Measurement with the EEG-Hat
4. Results
4.1. Quantification of the Contact Force and Hair Separation
4.2. Determination of the Shutter Angle
4.3. The Rotation Angle of the Ball Joint
4.4. EEG Measurement with the EEG-Hat
5. Discussion
5.1. Quantification of the Contact Force and Hair Separation
5.2. Determination of the Shutter Angle
5.3. The Rotation Angle of the Ball Joint
5.4. EEG Measurements Using the EEG-Hat
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Cheyne, D.O. MEG studies of sensorimotor rhythms: A review. Exp. Neurol. 2013, 245, 27–39. [Google Scholar] [CrossRef]
- Hribar, A.; Koritnik, B.; Munih, M. Phantom haptic device upgrade for use in fMRI. Med. Biol. Eng. Comput. 2009, 47, 677–684. [Google Scholar] [CrossRef]
- Fazli, S.; Mehnert, J.; Steinbrink, J.; Curio, G.; Villringer, A.; Müller, K.L.; Blankertz, B. Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage 2012, 59, 519–529. [Google Scholar] [CrossRef]
- Sourina, O.; Liu, Y.; Nguyen, M.K. Real-time EEG-based emotion recognition for music therapy. J. Multimodal User Interfaces 2012, 5, 27–35. [Google Scholar] [CrossRef]
- Tement, S.; Pahor, A.; Jaušovec, N. EEG alpha frequency correlates of burnout and depression: The role of gender. Biol. Psychol. 2016, 114, 1–12. [Google Scholar] [CrossRef]
- Teixeira, A.R.; Tomé, A.; Roseiro, L.; Gomes, A. Does music help to be more attentive while performing a task? A brain activity analysis. In Proceedings of the BIBM 2018: 2018 IEEE International Conference on Bioinformatics and Biomedicine, Madrid, Spain, 3–6 December 2018; pp. 1564–1570. [Google Scholar]
- Aftanas, L.I.; Reva, N.V.; Varlamov, A.A.; Pavlov, S.V.; Makhnev, V.P. Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: Temporal and topographic characteristics. Neurosci. Behav. Physiol. 2004, 34, 859–867. [Google Scholar] [CrossRef]
- Chen, X.; Pan, Z.; Wang, P.; Yang, X.; Liu, P.; You, X.; Yuan, J. The integration of facial and vocal cues during emotional change perception: EEG markers. Soc. Cogn. Affect. Neurosci. 2016, 11, 1152–1161. [Google Scholar] [CrossRef] [Green Version]
- Prichep, L.S.; John, E.R.; Ferris, S.H.; Reisberg, B.; Almas, M.; Alper, K.; Cancro, R. Quantitative EEG correlates of cognitive deterioration in the elderly. Neurobiol. Aging 1994, 15, 85–90. [Google Scholar] [CrossRef]
- Stevens, R.H.; Galloway, T.; Berka, C. EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2007; Volume 4511 LNCS, pp. 187–196. [Google Scholar]
- Kerous, B.; Skola, F.; Liarokapis, F. EEG-based BCI and video games: A progress report. Virtual Real. 2018, 22, 119–135. [Google Scholar] [CrossRef]
- Tromp, J.; Peeters, D.; Meyer, A.S.; Hagoort, P. The combined use of virtual reality and EEG to study language processing in naturalistic environments. Behav. Res. Methods 2018, 50, 862–869. [Google Scholar] [CrossRef] [Green Version]
- Amores, J.; Richer, R.; Zhao, N.; Maes, P.; Eskofier, B.M. Promoting relaxation using virtual reality, olfactory interfaces and wearable EEG. In Proceedings of the IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Las Vegas, NV, USA, 4–7 March 2018; Volume 2018, pp. 98–101. [Google Scholar]
- Flexer, A.; Gruber, G.; Dorffner, G. A reliable probabilistic sleep stager based on a single EEG signal. Artif. Intell. Med. 2005, 33, 199–207. [Google Scholar] [CrossRef]
- Cheng, Y.; Chen, C.; Decety, J. An EEG/ERP investigation of the development of empathy in early and middle childhood. Dev. Cogn. Neurosci. 2014, 10, 160–169. [Google Scholar] [CrossRef] [Green Version]
- Hedrich, T.; Pellegrino, G.; Kobayashi, E.; Lina, J.M.; Grova, C. Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG. Neuroimage 2017, 157, 531–544. [Google Scholar] [CrossRef] [PubMed]
- Okamoto, M.; Dana, H.; Sakamoto, K.; Takeo, K.; Shimizu, K.; Kohno, S.; Oda, I.; Isobe, S.; Suzuki, T.; Kohyama, K.; et al. Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. Neuroimage 2004, 21, 99–111. [Google Scholar] [CrossRef] [PubMed]
- Arai, M.; Kudo, Y.; Miki, N. Polymer-based candle-shaped microneedle electrodes for electroencephalography on hairy skin. Jpn. J. Appl. Phys. 2016, 55, 4–10. [Google Scholar] [CrossRef]
- Yoshida, Y.; Kudo, Y.; Hoshino, E.; Minagawa, Y.; Miki, N. Preparation-Free Measurement of Event-Related Potential in Oddball Tasks from Hairy Parts Using Candle-Like Dry Microneedle Electrodes. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; pp. 4685–4688. [Google Scholar]
- Onomoto, T.; Yoshida, Y.; Miki, N. Electroencephalogram Measurement in Adapting Process to Inverse Vision. In Proceedings of the International Conference on Electronics Packaging (ICEP), Niigata, Japan, 17–20 April 2019; pp. 134–137. [Google Scholar]
- Kudo, Y.; Arai, M.; Miki, N. Fatigue assessment by electroencephalogram measured with candle-like dry microneedle electrodes. Micro Nano Lett. 2017, 12, 545–549. [Google Scholar] [CrossRef]
- Takeuchi, S.; Ziegler, D.; Yoshida, Y.; Mabuchi, K.; Suzuki, T. Parylene flexible neural probes integrated with microfluidic channels. Lab. Chip 2005, 5, 519–523. [Google Scholar] [CrossRef] [PubMed]
- Li, G.; Wang, S.; Duan, Y.Y. Towards gel-free electrodes: A systematic study of electrode-skin impedance. Sens. Actuators B Chem. 2017, 241, 1244–1255. [Google Scholar] [CrossRef]
- Surangsrirat, D.; Intarapanich, A. Analysis of the meditation brainwave from consumer EEG device. In Proceedings of the Conference—IEEE SoutheastCon, Fort Lauderdale, FL, USA, 9–12 April 2015; Volume 2015. [Google Scholar]
- Clemente, M.; Rodríguez, A.; Rey, B.; Alcañiz, M. Assessment of the influence of navigation control and screen size on the sense of presence in virtual reality using EEG. Expert Syst. Appl. 2014, 41, 1584–1592. [Google Scholar] [CrossRef]
- Alchalcabi, A.E.; Eddin, A.N.; Shirmohammadi, S. More attention, less deficit: Wearable EEG-based serious game for focus improvement. In Proceedings of the IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH), Perth, WA, Australia, 2–4 April 2017; pp. 1–8. [Google Scholar]
- Ledwidge, P.; Foust, J.; Ramsey, A. Recommendations for Developing an EEG Laboratory at a Primarily Undergraduate Institution. J. Undergrad. Neurosci. Educ. 2018, 17, A10–A19. [Google Scholar]
- Zotev, V.; Phillips, R.; Yuan, H.; Misaki, M.; Bodurka, J. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. Neuroimage 2014, 85, 985–995. [Google Scholar] [CrossRef] [Green Version]
- Radüntz, T. Signal quality evaluation of emerging EEG devices. Front. Physiol. 2018, 9, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bonmassar, G.; Schwartz, D.P.; Liu, A.K.; Kwong, K.K.; Dale, A.M.; Belliveau, J.W. Spatiotemporal brain imaging of visual-evoked activity using interleaved EEG and fMRI recordings. Neuroimage 2001, 13, 1035–1043. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Homölle, S.; Oostenveld, R. Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions. J. Neurosci. Methods 2019, 326, 108378. [Google Scholar] [CrossRef] [PubMed]
- Zheng, W.; Aftreth, J.; Sessoms, P.; Cox, B. Validating Mobile Electroencephalographic Systems for Integration into the PhyCORE and Application in Clinical Settings; Naval Health Research Center: San Diego, CA, USA, 2016. [Google Scholar]
- Lin, C.T.; Chuang, C.H.; Huang, C.S.; Tsai, S.F.; Lu, S.W.; Chen, Y.H.; Ko, L.W. Wireless and wearable EEG system for evaluating driver vigilance. IEEE Trans. Biomed. Circuits Syst. 2014, 8, 165–176. [Google Scholar]
- Ratti, E.; Waninger, S.; Berka, C.; Ruffini, G.; Verma, A. Comparison of medical and consumer wireless EEG systems for use in clinical trials. Front. Hum. Neurosci. 2017, 11, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Klamer, S.; Elshahabi, A.; Lerche, H.; Braun, C.; Erb, M.; Scheffler, K.; Focke, N.K. Differences between MEG and High-Density EEG Source Localizations Using a Distributed Source Model in Comparison to fMRI. Brain Topogr. 2014, 28, 87–94. [Google Scholar] [CrossRef]
- Taylor, G.S.; Schmidt, C. Empirical evaluation of the Emotiv EPOC BCI headset for the detection of mental actions. Proc. Hum. Factors Ergon. Soc. 2012, 56, 193–197. [Google Scholar] [CrossRef]
- Lancheros-Cuesta, D.J.; Arias, J.L.R.; Forero, Y.Y.; Duran, A.C. Evaluation of e-learning activities with NeuroSky MindWave EEG Evaluación de actividades e-learning con NeuroSky MindWave EEG. In Proceedings of the Iberian Conference on Information Systems and Technologies (CISTI), Caceres, Spain, 13–16 June 2018; Volume 2018, pp. 1–6. [Google Scholar]
- Jochumsen, M.; Knoche, H.; Kidmose, P.; Kjær, T.W.; Dinesen, B.I. Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives. Front. Hum. Neurosci. 2020, 14, 1–10. [Google Scholar] [CrossRef]
- Nakanishi, M.; Wang, Y.T.; Wei, C.S.; Chiang, K.J.; Jung, T.P. Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses. IEEE Trans. Biomed. Eng. 2020, 67, 1105–1113. [Google Scholar] [CrossRef]
- Tauscher, J.P.; Schottky, F.W.; Grogorick, S.; Bittner, P.M.; Mustafa, M.; Magnor, M. Immersive EEG: Evaluating electroencephalography in virtual reality. In Proceedings of the 26th IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Osaka, Japan, 23–27 March 2019; pp. 1794–1800. [Google Scholar]
- Liu, Y.; Jiang, X.; Cao, T.; Wan, F.; Mak, P.U.; Mak, P.; Vai, M.I. Implementation of SSVEP based BCI with Emotiv EPOC. In Proceedings of the IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS, Tianjin, China, 2–4 July 2012; pp. 34–37. [Google Scholar]
- Mihajlovic, V.; Grundlehner, B.; Vullers, R.; Penders, J. Wearable, wireless EEG solutions in daily life applications: What are we missing? IEEE J. Biomed. Heal. Inf. 2015, 19, 6–21. [Google Scholar] [CrossRef] [PubMed]
- Motti, V.G.; Caine, K. Human factors considerations in the design of wearable devices. Proc. Hum. Factors Ergon. Soc. 2014, 58, 1820–1824. [Google Scholar] [CrossRef] [Green Version]
- Yang, H.; Yu, J.; Zo, H.; Choi, M. User acceptance of wearable devices: An extended perspective of perceived value. Telemat. Inf. 2016, 33, 256–269. [Google Scholar] [CrossRef]
- Przegalinska, A.; Ciechanowski, L.; Magnuski, M.; Gloor, P. Muse Headband: Measuring Tool or a Collaborative Gadget? In Collaborative Innovation Networks; Springer: Cham, Switzerland, 2018; pp. 93–101. [Google Scholar]
- Kawana, T.; Yoshida, Y.; Kudo, Y.; Miki, N. EEG-Hat with Candle-like Microneedle Electrode. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Berlin, Germany, 23–27 July 2019; pp. 1111–1114. [Google Scholar]
- Davis, S.P.; Landis, B.J.; Adams, Z.H.; Allen, M.G.; Prausnitz, M.R. Insertion of microneedles into skin: Measurement and prediction of insertion force and needle fracture force. J. Biomech. 2004, 37, 1155–1163. [Google Scholar] [CrossRef] [PubMed]
- Moronkeji, K.; Todd, S.; Dawidowska, I.; Barrett, S.D.; Akhtar, R. The role of subcutaneous tissue stiffness on microneedle performance in a representative in vitro model of skin. J. Control. Release 2017, 265, 102–112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alexander, H.; Miller, D.L. Determing Skin Thickness with Pulsed Ultra Sound. J. Investig. Dermatol. 1979, 72, 17–19. [Google Scholar] [CrossRef] [Green Version]
- Mammone, N.; la Foresta, F.; Morabito, F.C. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sens. J. 2012, 12, 533–542. [Google Scholar] [CrossRef]
- Durka, P.J.; Klekowicz, H.; Blinowska, K.J.; Szelenberger, W.; Niemcewicz, S. A simple system for detection of EEG artifacts in polysomnographic recordings. IEEE Trans. Biomed. Eng. 2003, 50, 526–528. [Google Scholar] [CrossRef]
- Higashi, Y.; Yokota, Y.; Naruse, Y. Signal correlation between wet and original dry electrodes in electroencephalogram according to the contact impedance of dry electrodes. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Seogwipo, Korea, 11–15 July 2017; pp. 1062–1065. [Google Scholar]
- Barry, R.J.; Clarke, A.R.; Johnstone, S.J.; Magee, C.A.; Rushby, J.A. EEG differences between eyes-closed and eyes-open resting conditions. Clin. Neurophysiol. 2007, 118, 2765–2773. [Google Scholar] [CrossRef]
- Barry, R.J.; de Blasio, F.M. EEG differences between eyes-closed and eyes-open resting remain in healthy ageing. Biol. Psychol. 2017, 129, 293–304. [Google Scholar] [CrossRef] [Green Version]
- Urigüen, J.A.; Garcia-Zapirain, B. EEG artifact removal—State-of-the-art and guidelines. J. Neural Eng. 2015, 12, 031001. [Google Scholar] [CrossRef] [PubMed]
- Cruz-Garza, J.G.; Brantley, J.A.; Nakagome, S.; Kontson, K.; Megjhani, M.; Robleto, D.; Contreras-Vidal, J.L. Deployment of mobile EEG technology in an art museum setting: Evaluation of signal quality and usability. Front. Hum. Neurosci. 2017, 11, 527. [Google Scholar] [CrossRef] [PubMed]
Appearance | Products (Company) [Reference] | Number of Channels | Electrode | Example of Application [Reference] |
---|---|---|---|---|
Net-type | GES400 (Electrical Geodesics) [47] | 256 | Saline | Clinical [27] High-density EEG [35] |
R-Net (Brain Products) (Press release) [31] | 128 | Saline | ||
Cap-type | actiCAP (Brain Products) [31] | 256 | Dry | Virtual reality [40] Neurofeedback [28] Brain imaging [30] |
g.Nautilus research (g.tec) [29] | 64 | Saline or Gel | ||
Quick-Cap (Compumedics Neuroscan) [38] | 32 | Gel | ||
Headgear-type | Ultracortex Mark Ⅳ (Open BCI) [38] | 32 | Dry | BCI [41] Virtual reality [25] Game [26] Mental health [36] Driver’s state [33] |
32 Trilobite (Mindo) [29] | 32 | Dry | ||
Quick-30 (CGX) [39] | 30 | Dry | ||
DSI-24 (Wearable Sensing) [32] | 24 | Dry | ||
EPOC+ (Emotiv) [29] | 14 | Saline | ||
BR8+ (BRI) [29] | 8 | Dry | ||
Headband-type | Muse (InteraXon) [34] | 2 | Dry | Meditation [24] |
MindWave Mobile2 (NeuroSky) [34] | 1 | Dry | Education [37] |
Participant | SNR of 5° | SNR of 10° | Measurement Order | Which Was More Painful? |
---|---|---|---|---|
1 | 2.5 | −0.4 | 5° → 10° | 10° |
2 | 9.4 | −5.9 | 5° → 10° | 10° |
3 | 7.9 | 3.5 | 5° → 10° | 10° |
4 | 9.2 | 9.0 | 5° → 10° | Did not feel any difference |
5 | 10 | 6.8 | 5° → 10° | Slightly 10° |
6 | 14 | 13 | 10° → 5° | Did not feel any difference |
7 | −0.2 | −1.3 | 10° → 5° | 10° |
8 | 6.2 | 7.9 | 10° → 5° | Did not feel any difference |
9 | 12 | −1.2 | 10° → 5° | Did not feel any difference |
10 | 3.6 | 1.8 | 10° → 5° | Slightly 10°, almost no difference |
mean ± SD | 7.5 ± 4.5 | 3.4 ± 5.6 |
Participant | F4 | F3 | Cz | O2 | O1 | |
---|---|---|---|---|---|---|
1 | Open | −5.5 | −4.6 | −5.5 | 0.4 | −3.6 |
Close | −1.1 | 0.2 | −3.8 | 5.0 | 2.1 | |
2 | Open | −6.5 | −5.1 | 0.1 | −8.9 | −4.2 |
Close | −5.7 | 3.2 | 3.8 | 6.0 | 5.3 | |
3 | Open | −3.6 | −6.3 | −0.9 | −3.6 | −3.0 |
Close | 1.2 | −5.3 | 1.5 | 3.0 | 5.8 | |
4 | Open | −5.9 | −2.5 | −3.4 | −6.6 | −4.6 |
Close | −1.5 | 4.6 | 2.8 | −6.6 | 3.4 |
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Kawana, T.; Yoshida, Y.; Kudo, Y.; Iwatani, C.; Miki, N. Design and Characterization of an EEG-Hat for Reliable EEG Measurements. Micromachines 2020, 11, 635. https://doi.org/10.3390/mi11070635
Kawana T, Yoshida Y, Kudo Y, Iwatani C, Miki N. Design and Characterization of an EEG-Hat for Reliable EEG Measurements. Micromachines. 2020; 11(7):635. https://doi.org/10.3390/mi11070635
Chicago/Turabian StyleKawana, Takumi, Yuri Yoshida, Yuta Kudo, Chiho Iwatani, and Norihisa Miki. 2020. "Design and Characterization of an EEG-Hat for Reliable EEG Measurements" Micromachines 11, no. 7: 635. https://doi.org/10.3390/mi11070635
APA StyleKawana, T., Yoshida, Y., Kudo, Y., Iwatani, C., & Miki, N. (2020). Design and Characterization of an EEG-Hat for Reliable EEG Measurements. Micromachines, 11(7), 635. https://doi.org/10.3390/mi11070635