The Status of Textile-Based Dry EEG Electrodes

• Autorzy: Tseghai, Granch Berhe , Malengier, Benny , Fante, Kinde Anlay , Van, Langenhove Lieva
• Języki publikacji: EN

Abstrakty

EN

Electroencephalogram (EEG) is the biopotential recording of electrical signals generated by brain activity. It is useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/alertness in the field. We provide a review of textile-based EEG. Most of the developed textile-based EEGs remain on shelves only as published research results due to a limitation of flexibility, stickability, and washability, although the respective authors of the works reported that signals were obtained comparable to standard EEG. In addition, nearly all published works were not quantitatively compared and contrasted with conventional wet electrodes to prove feasibility for the actual application. This scenario would probably continue to give a publication credit, but does not add to the growth of the specific field, unless otherwise new integration approaches and new conductive polymer composites are evolved to make the application of textile-based EEG happen for bio-potential monitoring.

Słowa kluczowe

PL

elektroencefalogram; monitorowanie aktywności mózgu; elektrody tekstylne

EN

electroencephalogram; brain activity monitoring; textile-based electrode

• Czasopismo: Autex Research Journal
• Rocznik: 2021
• Tom: Vol. 21, no. 1
• Strony: 63--70
• Opis fizyczny: Bibliogr 62 poz.

Twórcy

autor

Tseghai, Granch Berhe

• Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent, Belgium,
• Jimma Institute of Technology, Jimma University, Jimma, Ethiopia

autor

Malengier, Benny

• Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent, Belgium

autor

Fante, Kinde Anlay

• Jimma Institute of Technology, Jimma University, Jimma, Ethiopia

autor

Van, Langenhove Lieva

• Department of Materials, Textiles and Chemical Engineering, Ghent University, Ghent, Belgium

Bibliografia

• [1] Chetna, M., Shah, S. (2008). Diseases of the brain and nervous system caution. Team Spirit (India) Pvt. Ltd (Ahmedabad).
• [2] Matilla-dueñas, A., Corral-juan, M. (2017). Rare diseases epidemiology: update and overview, vol. 1031, Springer International Publishing.
• [3] Beniczky, S., Polster, T., Kjaer, T. W., Hjalgrim, H. (2013). Detection of generalized tonic–clonic seizures by a wireless wrist accelerometer: A prospective, multicenter study. Epilepsia, 54(4), 1–4.
• [4] Kusmakar, S., Karmakar, C. K., Yan, B., O’Brien, T. J., Muthuganapathy, R., Palaniswami, M. (2019). Automated detection of convulsive seizures using a wearable accelerometer device. IEEE Transactions on Biomedical Engineering, 66(2), 421–432.
• [5] Halford, J. J., Sperling, M. R., Nair, D. R., Dlugos, D. J., Tatum, W. O., et al. (2017). Detection of generalized tonic–clonic seizures using surface electromyographic monitoring. Epilepsia, 58(11), 1861–1869.
• [6] Lee, J. W. (2018). Real-time non-EEG convulsive seizure detection devices: they work; now what? Epilepsy Currents, 18(3), 164–166.
• [7] Strong, V., Brown, S. W., Walker, R. (1999). Seizure-alert dogs — fact or fiction ? Seizures, 8, 62–65.
• [8] Miller, J. W. (2010). Are generalized tonic–clonic seizures really ‘generalized’? Epilepsy Currents, 10(4), 80–81.
• [9] Hua, H., Tang, W., Xu, X., Feng, D. D., Shu, L. (2019). Flexible multi-layer semi-dry electrode for scalp EEG measurements at Hairy sites. Micromachines, 10(518), 1–13.
• [10] Sun, Y., Lo, F. P. W., Lo, B. (2019). EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert System with Applications, 125, 259–267.
• [11] Khalaf, A., Sejdic, E., Akcakaya, M. (2019). EEG-fTCD Hybrid brain-computer interface using template matching and wavelet decomposition. Journal of Neural Engineering, 16(3).
• [12] Radhakrishnan, J. K., Nithila, S., Kartik, S. N., Bhuvana, T., Kulkarni, G. U., et al. (2018). A novel, needle-array dry-electrode with stainless steel micro-tips, for electroencephalography monitoring. Journal of Medical Device, 12(4), 1–7.
• [13] Wunder, S., Hunold, A., Fiedler, P., Schlegelmilch, F., Schellhorn, K., et al. (2018). Novel bifunctional cap for simultaneous electroencephalography and transcranial electrical stimulation. Science Reports, 8(1), 1–11.
• [14] Alawieh, H., Hammoud, H., Haidar, M., Nassralla, M. H., El-Hajj, A. M., et al. (2016). Patient-aware adaptive ngram-based algorithm for epileptic seizure prediction using EEG signals. 2016 IEEE 18th International Conference on e-Health Networking, Application and Services Healthcom, 2016, 1–6.
• [15] Spies, R., Gassert, R. (2019). A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG. Journal of Neural Engineering, 16(1), 016019.
• [16] Al Ghayab, H. R., Li, Y., Siuly, S., Abdulla, S. (2018). Epileptic seizures detection in EEGs blending frequency domain with information gain technique. Soft Computing, 23(1), 227–239.
• [17] Chen, Y. J., Lin, Y. S., Chiueh, H. (2016). EEG recording frontend circuitry for epileptic seizure detection headband. 2016 IEEE Healthcare Innovations and Point-of-Care Technologies, Conference HI-POCT, 2016, 42–45.
• [18] Wang, F., Li, G., Chen, J., Duan, Y. (2016). Novel semi-dry electrodes for brain–computer interface applications. Journal of Neural Engineering, 13(4), 046021.
• [19] Kannan, R., Ali, S. S. A., Farah, A., Adil, S. H., Khan, A. (2017). Smart wearable EEG sensor. Procedia Computer Science, 105(December 2016), 138–143.
• [20] Kappel, S. L., Rank, M. L., Toft, H. O., Andersen, M., Kidmose, P. (2018). Dry-contact electrode ear-EEG. IEEE Transactions on Biomedical Engineering, 66(1), 150–158.
• [21] Xing, X., Wang, Y., Pei, W., Guo, X., Liu, Z., et al. (2018). A high-speed SSVEP-based BCI using dry EEG electrodes. Scientific Reports, 8(1), 1–10.
• [22] Advanced Brain Monitoring. (2019). X series – EEG wireless monitoring. [Online] . Web site: https://www.advancedbrainmonitoring.com/.
• [23] Neuro:On. (2019). Neuro: on smart sleep mask. [Online] . Web site: https://neuroonopen.com/.
• [24] OpenBCI. (2019). Ultracortex ‘mark IV’ EEG headset. [Online] . Web site: https://openbci.com/.
• [25] Neurosky. (2019). ThinkGearTM AM (TGAM EEG biosensor). [Online]. Web site: http://neurosky.com/biosensors/eeg-sensor/.
• [26] PLX Devices. (2019). XWave EEG. [Online]. Web site: https://www.plxdevices.com/.
• [27] Science Division. (2019). EEG SENSOR - T9305M. [Online]. Web site: http://www.thoughttechnology.com/sciencedivision/pages/products/eegflex.html.
• [28] Plux. (2019). SENS-EEG-UCE6. [Online] . Web site: https://plux.info/.
• [29] Lee, E., Cho, G. (2019). PU nanoweb-based textile electrode treated with single-walled carbon nanotube/silver nanowire and its application to ECG monitoring. Smart Materials and Structures, 28(4), 045004.
• [30] Ankhili, A., Tao, X., Koncar, V., Coulon, D., Tarlet, J. (2019). Ambulatory evaluation of ECG signals obtained using washable textile-based electrodes made with. Sensors, 19(416), 13.
• [31] Kang, T., Park, J., Yun, G., Hee, H., Lee, H. (2019). A real-time humidity sensor based on a microwave oscillator with conducting polymer PEDOT : PSS film. Sensors Actuators B. Chemical, 282, 145–151.
• [32] Achilli, A., Bonfiglio, A., Pani, D. (2018). Design and characterization of screen-printed textile electrodes for ECG monitoring. IEEE Sensors Journal, 18(10), 4097–4107.
• [33] An, X., Stylios, G. K. (2018). A hybrid textile electrode for electrocardiogram (ECG) measurement and motion tracking. Materials (Basel), 11(10), pii: E1887.
• [34] Wu, W., Pirbhulal, S., Sangaiah, A. K., Mukhopadhyay, S. C., Li, G. (2018). Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Generation Computer Systems, 86, 515–526.
• [35] Das, P. S., Park, J. Y. (2017). A flexible touch sensor based on conductive elastomer for biopotential monitoring applications. Biomedical Signal Processing Control, 33, 72–82.
• [36] Lee, S., Kim, M. O., Kang, T., Park, J., Choi, Y. (2018). Knit band sensor for myoelectric control of surface EMG-based prosthetic hand. IEEE Sensors Journal, 18(20), 8578–8586.
• [37] Shafti, A., Ribas Manero, R. B., Borg, A. M., Althoefer, K., Howard, M. J. (2017). Embroidered electromyography: a systematic design guide. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(9), 1472–1480.
• [38] Yand, G., Deng, J., Pang, G., Zhang, H., Li, J., et al. (2018). An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning. IEEE Journal of Translational Engineering in Health and Medicine, 6(March, 2018).
• [39] Paul, G. M., Cao, F., Torah, R., Yang, K., Beeby, S., et al. (2014). A smart textile based facial EMG and EOG computer interface. IEEE Sensors Journal, 14(2), 393–400.
• [40] Niijima, A., Isezaki, T., Aoki, R., Watanabe, T. (2017). hitoeCap: wearable EMG sensor for monitoring masticatory muscles with PEDOT-PSS textile electrodes. In: ISWC ‘17 Proceedings of the 2017 ACM International Symposium on Wearable Computers, Maui, Hawaii. September 11–15, 2017, 215–220.
• [41] Kang, T. H., Merritt, C., Karaguzel, B., Wilson, J., Franzon, P., et al. (2006). Sensors on textile substrates for home-based healthcare monitoring. Conference Proceedings - 1st Transdisciplinary Conference on Distributed Diagnosis Home Healthcare, D2H2 2006, vol. 2006, Arlington, VA, USA, 2–4 April 2006, 5–7.
• [42] Wu, J., Jia, W., Xu, C., Gao, D., Sun, M. (2017). Impedance analysis of ZnO nanowire coated dry EEG electrodes. Journal of Biomedical Engineering Informatics, 3(1), 44.
• [43] Li, H., Chen, X., Cao, L., Zhang, C., Tang, C., et al. (2017). Textile-based ECG acquisition system with capacitively coupled electrodes. Transactions of the Institute of Measurement and Control, 39(2), 141–148.
• [44] Kirkup, L., Searle, A. (2000). A direct comparison of wet, dry and insulating bioelectric recording electrodes. Physiological Measurement, 21(2), 271–283.
• [45] Lopez-Gordo, M. A., Sanchez Morillo, D., Pelayo Valle, F. (2014). Dry EEG electrodes. Sensors (Switzerland), 14(7), 12847–12870.
• [46] Löfuede, J., Seoane, F., Thordstein, M. (2010). Soft textile electrodes for EEG monitoring. In: Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB, Corfu, Greece, 3–5 November 2010, 4–7.
• [47] Lin, C.-T., Liao, L.-D., Liu, Y.-H., Wang, I.-J., Lin, B.-S., et al. (2011). Novel dry polymer foam electrodes for long-term EEG measurement. IEEE Transactions on Biomedical Engineering, 58(5), 1200–1207.
• [48] Salvo, P., Raedt, R., Carrette, E., Schaubroeck, D., Vanfleteren, J., et al. (2012). A 3D printed dry electrode for ECG/EEG recording. Sensors Actuators, A: Physical, 174(1), 96–102.
• [49] Löfhede, J., Seoane, F., Thordstein, M. (2012). Textile electrodes for EEG recording - a pilot study. Sensors (Switzerland), 12(12), 16907–16919.
• [50] Kumar, N. M., Thilagavathi, G. (2014). Design and development of textile electrodes for EEG measurement using copper plated polyester fabrics. Journal of Textile and Apparel, Technology and Management, 8(4), 1–8.
• [51] Sahi, A., Rai, P., Oh, S., Ramasamy, M., Harbaugh, R. E., et al. (2014). Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system. Nanosensors, Biosensors, Info-Tech Sensors Systems, 9060, 1–9.
• [52] Muthukumar, N., Thilagavathi, G., Kannaian, T. (2016). Polyaniline-coated foam electrodes for electroencephalography (EEG) measurement. The Journal of Textile Institute, 107(3), 283–290.
• [53] Peng, H. L., Liu, J.-Q., Tian, H.-G., Dong, Y.-Z., Yang, B., et al. (2016). A novel passive electrode based on porous Ti for EEG recording. Sensors Actuators, B Chem., 226, 349–356.
• [54] Gao, K. P., Yang, H. J., Wang, X. L., Yang, B., Liu, J. Q. (2018). Soft pin-shaped dry electrode with bristles for EEG signal measurements. Sensors Actuators, A Physical, 283, 348–361.
• [55] Renz, A. F., Reichmuth, A. M., Stauffer, F., Thompson-Steckel, G., Janos, V. (2018). A guide towards long-term functional electrodes interfacing neuronal tissue. Journal of Neural Engineering, 15(6), 061001.
• [56] Zerafa, R., Camilleri, T., Falzon, O., Camilleri, K. P. (2018). To train or not to train ? A survey on training of feature extraction methods for SSVEP-based BCIs. Journal of Neural Engineering, 15(5), 051001.
• [57] Senn, P., Shepherd, R. K., Fallon, J. B. (2018). Focused electrical stimulation using a single current source. Journal of Neural Engineering, 15(5), 056018.
• [58] Craik, A., He, Y., Contreras-Vidal, J. L. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(3), 031001.
• [59] Sadatnejad, K., Rahmati, M., Rostami, R. (2019). EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices. Journal of Neural Engineering, 16(3), 036016.
• [60] Zhang, X., D’Arcy, R., Menon, C. (2019). Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study. Journal of Neural Engineering, 16(3), 036013.
• [61] Higgins, G., Faul, S., Glavin, M., Jones, E., McGinley, B., et al. (2013). The effects of lossy compression on diagnostically relevant seizure information in EEG signals. IEEE Journal of Biomedical Health Informatics, 17(1), 121–127.
• [62] Golparvar, A. J., Yapici, M. K. (2018). Electrooculography by wearable graphene textiles. IEEE Sensors Journal, 18(21), 8971–8978.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).

Identyfikatory

DOI

10.2478/aut-2019-0071