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Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission

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Abstract

Brain–computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

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References

  1. Liao LD et al (2012) “Biosensor technologies for augmented brain-computer interfaces in the next decades,” (in English). Proceedings of IEEE 100:1553–1566. https://doi.org/10.1109/Jproc.2012.2184829

    Article  CAS  Google Scholar 

  2. Vaughan TM et al (2006) The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Trans Neural Syst Rehabil Eng 14(2):229–233. https://doi.org/10.1109/TNSRE.2006.875577

    Article  PubMed  Google Scholar 

  3. Bayram A et al (2011) Simultaneous EEG/fMRI analysis of the resonance phenomena in steady-state visual evoked responses. Clin EEG Neurosci 42(2):98–106. https://doi.org/10.1177/155005941104200210

    Article  PubMed  Google Scholar 

  4. Lin C-T et al (2008) Noninvasive neural prostheses using mobile and wireless EEG. Proc IEEE 96(7):1167–1183. 

    Article  Google Scholar 

  5. Miniussi C, Thut G (2010) Combining TMS and EEG offers new prospects in cognitive neuroscience. Brain Topogr 22(4):249–256. https://doi.org/10.1007/s10548-009-0083-8

    Article  PubMed  Google Scholar 

  6. Zander TO et al (2011) A Dry EEG-System for Scientific Research and Brain-Computer Interfaces. Front Neurosci 5:53. https://doi.org/10.3389/fnins.2011.00053

    Article  PubMed  PubMed Central  Google Scholar 

  7. Srinivasan N (2007) Cognitive neuroscience of creativity: EEG based approaches. Methods 42(1):109–116. https://doi.org/10.1016/j.ymeth.2006.12.008

    Article  CAS  PubMed  Google Scholar 

  8. Lin CT et al (2008) Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver’s drowsiness detection and warning. IEEE Trans Biomed Eng 55(5):1582–1591. https://doi.org/10.1109/TBME.2008.918566

    Article  PubMed  Google Scholar 

  9. Marcelis K, Vercruyssen M, Nicu E, Naert I, Quirynen M (2012) Sleeping vs. loaded implants, long-term observations via a retrospective analysis. Clin Oral Implants Res 23(9):1118–1122. https://doi.org/10.1111/j.1600-0501.2011.02263.x

    Article  CAS  PubMed  Google Scholar 

  10. Kim YS, Baek HJ, Kim JS, Lee HB, Choi JM, Park KS (2009) Helmet-based physiological signal monitoring system. Eur J Appl Physiol 105(3):365–372. https://doi.org/10.1007/s00421-008-0912-6

    Article  PubMed  Google Scholar 

  11. Fonseca C et al (2007) A novel dry active electrode for EEG recording. IEEE Trans Biomed Eng 54(1):162–165. https://doi.org/10.1109/TBME.2006.884649

    Article  CAS  PubMed  Google Scholar 

  12. Griss P, Tolvanen-Laakso HK, Merilainen P, Stemme G (2002) Characterization of micromachined spiked biopotential electrodes. IEEE Trans Biomed Eng 49(6):597–604. https://doi.org/10.1109/TBME.2002.1001974

    Article  PubMed  Google Scholar 

  13. Nijboer F et al (2008) “A P300-based brain-computer interface for people with amyotrophic lateral sclerosis,” ). Clin Neurophysiol 119(8):1909–1916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hoffmann U, Vesin J-M, Ebrahimi T, Diserens K (2008) An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods 167(1):115–125. https://doi.org/10.1016/j.jneumeth.2007.03.005

    Article  PubMed  Google Scholar 

  15. Chan MKL, Yeung WKY., Yu JKP., Ng SSW., and Tong RKY (2022) Exploratory Study on the Clinical use of EEG for the People with Chronic Stroke and Their Correlation with the Neuropsychological Outcome. Clin EEG Neurosci, p. 15500594221074858. https://doi.org/10.1177/15500594221074858

  16. Sutcliffe L, Lumley H, Shaw L, Francis R, Price CI (2022) Surface electroencephalography (EEG) during the acute phase of stroke to assist with diagnosis and prediction of prognosis: a scoping review. BMC Emerg Med 22(1):29. https://doi.org/10.1186/s12873-022-00585-w

    Article  PubMed  PubMed Central  Google Scholar 

  17. Parr JVV, Vine SJ, Wilson MR, Harrison NR, Wood G (2019) Visual attention, EEG alpha power and T7-Fz connectivity are implicated in prosthetic hand control and can be optimized through gaze training. J Neuroeng Rehabil 16(1):52. https://doi.org/10.1186/s12984-019-0524-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tsao YC, Cheng FJ, Li YH, Liao LD (2022) An IoT-Based Smart System with an MQTT Broker for Individual Patient Vital Sign Monitoring in Potential Emergency or Prehospital Applications. Emerg Med Int, 2022. https://doi.org/10.1155/2022/7245650

  19. Chen C et al (2021) “EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System,” (in English). J Med Biol Eng 41(2):155–164. https://doi.org/10.1007/s40846-020-00596-7

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lance BJ, Kerick SE, Ries AJ, Oie KS, McDowell K (2012) Brain-Computer Interface Technologies in the Coming Decades. Proceed IEEE 100:1585–1599. https://doi.org/10.1109/jproc.2012.2184830

    Article  CAS  Google Scholar 

  21. Ku CJ, Wang Y, Chang CY, Wu MT, Dai ST, and Liao LD (2022) Noninvasive blood oxygen, heartbeat rate, and blood pressure parameter monitoring by photoplethysmography signals. Heliyon, p. e11698

  22. Duan XY, Guo SJ, Chen LL, Wang MG (2022) A P300 Brain-Computer Interface for Lower Limb Robot Control Based on Tactile Stimulation (in English). J Med Biol Eng. https://doi.org/10.1007/s40846-022-00766-9

    Article  Google Scholar 

  23. Wu JY, Ching CTS, Wang HMD, Liao LD (2022) Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications. Biosensors-Basel, 12:12. https://doi.org/10.3390/bios12121097

  24. Lin CT, Jiang WL, Chen SF, Huang KC, Liao LD (2021) Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation. Biosensors-Basel, 11:9. https://doi.org/10.3390/bios11090343

  25. Liao LD, Wang IJ, Chen SF, Chang JY, Lin CT (2011) “Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation,” (in English). Sensors 11(6):5819–5834. https://doi.org/10.3390/s110605819

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wu SL, Liao LD, Lu SW, Jiang WL, Chen SA, Lin CT (2013) Controlling a human-computer interface system with a novel classification method that uses electrooculography signals (in eng). IEEE Trans Biomed Eng, Research Support, Non-U.S. Gov’t 60(8):2133–41. https://doi.org/10.1109/TBME.2013.2248154

    Article  Google Scholar 

  27. Chi YM, Jung TP, Cauwenberghs G (2010) Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev Biomed Eng 3:106–119. https://doi.org/10.1109/RBME.2010.2084078

    Article  PubMed  Google Scholar 

  28. Casson A, Yates D, Smith S, Duncan J, Rodriguez-Villegas E (2010) Wearable Electroencephalography. IEEE Eng Med Biol Mag 29(3):44–56. https://doi.org/10.1109/MEMB.2010.936545

    Article  PubMed  Google Scholar 

  29. Alba NA, Sclabassi RJ, Sun M, Cui XT (2010) “Novel hydrogel-based preparation-free EEG electrode,” (in eng). IEEE Trans Neural Syst Rehabil Eng 18(4):415–423. https://doi.org/10.1109/TNSRE.2010.2048579

    Article  PubMed  Google Scholar 

  30. Yu YH, Lu SW, Liao LD, Lin CT (2014) Design, Fabrication, and Experimental Validation of Novel Flexible Silicon-Based Dry Sensors for Electroencephalography Signal Measurements. IEEE J Transl Eng Health Med 2:2700307. https://doi.org/10.1109/JTEHM.2014.2367518

    Article  PubMed  Google Scholar 

  31. Sadaghiani S, Brookes MH, Baillet S (2022) Connectomics of human electrophysiology. Neuroimage, 247. https://doi.org/10.1016/j.neuroimage.2021.118788

  32. Roberto M (2010) The electrode–skin interface and optimal detection of bioelectric signals. Physiol Measure 31(10). https://doi.org/10.1088/0967-3334/31/10/e01

  33. Thakor NV (1999) Biopotentials and electro-physiology measurement. in The measurement, Instrumentation, and Sensors Handbook: CRC Press, ch. XI, pp 74–1. https://doi.org/10.1201/b15664-72

  34. Fiedler P, Fonseca C, Supriyanto E, Zanow F, Haueisen J (2022) “A high-density 256-channel cap for dry electroencephalography,”. Hum Brain Mapp 43(4):1295–1308. https://doi.org/10.1002/hbm.25721

    Article  PubMed  Google Scholar 

  35. Jing H, Takigawa M (2000) Low sampling rate induces high correlation dimension on electroencephalograms from healthy subjects. Psychiatry Clin Neurosci 54(4):407–412. https://doi.org/10.1046/j.1440-1819.2000.00729.x

    Article  CAS  PubMed  Google Scholar 

  36. Gatzke, R. "Biomedical Electrode Technology: Theory and Practice." (1974):99–116.

  37. J. G. Webster (ed.), Medical instrumentation: application and design. 3rd ed. New York: John Wiley & Sons, 1998.

  38. Alharbi, A.G.; Kulkarni, J.; Desai, A.; Sim, C.-Y.-D.; Poddar, A. A Multi-Slot Two-Antenna MIMO with High Isolation for Sub-6 GHz 5G/IEEE802.11ac/ax/C-Band/X-Band Wireless and Satellite Applications. Electronics 2022(11)473. https://doi.org/10.3390/electronics11030473

  39. Gollee H, Volosyak I, McLachlan IJ, Hunt KJ, Gräser A (2010) An SSVEP-based brain-computer interface for the control of functional electrical stimulation. IEEE Trans Biomed Eng 57(8):1847–1855. https://doi.org/10.1109/TBME.2010.2043432

    Article  PubMed  Google Scholar 

  40. Ortner R, Allison BZ, Korisek G, Gaggl H, Pfurtscheller G (2010) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 19(1):1–5. https://doi.org/10.1109/TNSRE.2010.2076364

    Article  PubMed  Google Scholar 

  41. Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR (2006) “A P300 event-related potential brain-computer interface (BCI): The effects of matrix size and inter stimulus interval on performance,” (in English). Biol Psychol 73(3):242–252. https://doi.org/10.1016/j.biopsycho.2006.04.007

    Article  PubMed  Google Scholar 

  42. Sadeh B, Podlipsky I, Zhdanov A, Yovel G (2010) Event-related potential and functional MRI measures of face-selectivity are highly correlated: a simultaneous ERP-fMRI investigation. Hum Brain Mapp 31(10):1490–1501. https://doi.org/10.1002/hbm.20952

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the National Science and Technology Council of The Republic of China, Taiwan, for financially supporting this work under contract no. 109-2221-E-009-050-MY2 and 110-2221-E-A49-130-MY2. A part of this research was also supported by the Australian Research Council (ARC) under discovery grant DP210101093 and DP220100803, the Australia Defence Innovation Hub under Contract No. P18-650825, and the UTS Human-Centric AI Centre funding sponsored by GrapheneX (2023-2031); by the National Health Research Institutes of Taiwan under grant numbers NHRI-EX111-11111EI, and NHRI-EX111-11129EI and by the Ministry of Health and Welfare of Taiwan under grant numbers MOHW 112-0324-01-30-06 and MOHW 113-0324-01-30-11, and by the Metal Industries Research & Development Centre under grant number 112-EC-17-A-22-1851.

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Authors and Affiliations

  1. Human-centric AI Centre (HAI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Chin-Teng Lin

  2. Australia Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Chin-Teng Lin

  3. Brain Science and Technology Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Chin-Teng Lin & Kuan-Chih Huang

  4. Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan

    Yuhling Wang, Sheng-Fu Chen & Lun-De Liao

  5. Department of Electrical Engineering, National United University, Miaoli, Taiwan

    Yuhling Wang

  6. Institute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Kuan-Chih Huang

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Lin, CT., Wang, Y., Chen, SF. et al. Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission. Med Biol Eng Comput 61, 3003–3019 (2023). https://doi.org/10.1007/s11517-023-02879-y

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