http://iet.metastore.ingenta.com
1887

NeuroMonitor: a low-power, wireless, wearable EEG device with DRL-less AFE

NeuroMonitor: a low-power, wireless, wearable EEG device with DRL-less AFE

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Circuits, Devices & Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Electroencephalography (EEG) is an effective tool to non-invasively capture brain responses. Traditional EEG analogue front end (AFE) requires a driven right leg (DRL) circuit that restricts the number of channels of the device. The authors are proposing a new ‘DRL-less’ AFE design, and have developed a wearable EEG device (NeuroMonitor), which is small, low-power, wireless, and battery operated. The EEG device with two independent channels was fabricated on an 11.35 cm2 PCB that contained a system-on-a-chip microcontroller, a low-noise instrument amplifier, a low-power Bluetooth module, a microSD, a microUSB, and a LiPo battery. The DRL circuit was eliminated by utilising the high CMRR instrument amplifier with differential inputs, and followed by a modified high-Q active Twin-T notch filter ((fc Notch = 60 Hz,  − 38 dB). The signal was conditioned with a band-pass filter composed of a two-stage 2nd-order Chebyshev-I Sallen-Key low-pass filter cascaded with a passive 2nd-order low-pass filter (fc LP = 125 Hz) and a 1st-order passive high-pass filter (fc HP = 0.5 Hz). Finally, the signal was amplified to achieve an overall gain of 55.84 dB, and digitised with a 16-bit delta-sigma ADC (256 sps). The prototype weighs 41.8 gm, and has been validated against a research-grade EEG system (Neuroscan).

References

    1. 1)
      • 1. He, B., Coleman, T., Genin, G.M., et al: ‘Grand challenges in mapping the human brain: NSF workshop report’, IEEE Trans. Biomed. Eng., 2013, 60, (11), pp. 29832992.
    2. 2)
      • 2. Zheng, Y., Ding, X., Poon, C.C.Y., et al: ‘Unobtrusive sensing and wearable devices for health informatics’, IEEE Trans. Biomed. Eng.., 2014, 61, (5), pp. 15381554.
    3. 3)
      • 3. Alemdar, H., Ersoy, C.: ‘Wireless sensor networks for healthcare: a survey’, Comput. Netw., 2010, 54, pp. 26882710.
    4. 4)
      • 4. Lin, R., Lee, R., Tseng, C., et al: ‘Design and implementation of wireless multi-channel EEG recording system and study of EEG clustering method’, Biomed Eng. Appl. Basis Commun., 2006, 18, (6), pp. 276283.
    5. 5)
      • 5. Tapia, M., Intille, S., Lopez, L., et al: ‘The design of a portable kit of wireless sensors for naturalistic data collection’. Proc. Intl. Conf. Pervasive Comp, 2006, pp. 117134.
    6. 6)
      • 6. Casson, A.J., Yates, D.C., Smith, S.J.M., et al: ‘Wearable Electroencaphalography’, IEEE Eng. Med. Biol. Mag., 2010, pp. 4457.
    7. 7)
      • 7. Patel, S., Park, H., Bonato, P., et al: ‘A review of wearable sensors and systems with application in rehabilitation’, J. Neuroeng. Rehabil., 2012, 9, (21), pp. 117.
    8. 8)
      • 8. Morshed, B.I., Massa, A.: ‘Cutting-edge technology for a cognitive load performance assessment system’, MEDS Mag., 2013, pp. 1618.
    9. 9)
      • 9. Morshed, B.I., Khan, A.: ‘A brief review of technologies and challenges to monitor brain activities’, J. Bioeng. Biomed. Sci., 2014, 4, (1), pp. 110.
    10. 10)
      • 10. Chi, Y.M., Jung, T., Cauwenberghs, G.: ‘Dry-contact and noncontact biopotential electrodes: methodological review’, IEEE Rev. Biomed. Eng. Front. Neurosci., 2010, 3, pp. 106119.
    11. 11)
      • 11. Lin, C., Ko, L., Chiou, J., et al: ‘Noninvasive neural prosthesis using mobile and wireless EEG’, Proc. IEEE, 2008, 96, (7), pp. 11671183.
    12. 12)
      • 12. Zhu, L., Chen, H., Zhang, X., et al: ‘Design of portable multi-channel EEG signal acquisition system’. 2nd Intl. Conf. on BioMedical Engineering and Informatics (BMEI), Tianjin, China, October 2009, pp. 14.
    13. 13)
      • 13. Gnecchi, J.A.G., Lara, L.R.S., Garcia, J.C.H.: ‘Design and construction of an EEG data acquisition system for measurement of auditory evoked potentials’. Electronics, Robotics and Automotive Mechanics Conf., Morelos, Mexico, 2008, pp. 547552.
    14. 14)
      • 14. Debener, S., Minow, F., Emkes, R., et al: ‘How about taking a low-cost, small, and wireless EEG for a walk?’, Psychophysiology, 2012, 49, (11), pp. 16171621.
    15. 15)
      • 15. Gargiulo, G., Bifulco, P., Calvo, R.A., et al: ‘A mobile EEG system with dry electrodes’, IEEE Biomedical Circuits and Systems Conf., Baltimore, MD, 2008, pp. 273276.
    16. 16)
      • 16. Schuyler, R., White, A., Staley, K., et al: ‘Epileptic seizure detection’, IEEE Eng. Med. Biol. Mag., 2007, 26, (2), pp. 7481.
    17. 17)
      • 17. Lin, C., Ko, L., Chiou, J., et al: ‘Noninvasive neural prostheses using mobile and wireless EEG’, Proc. IEEE, 2008, 96, (7), pp. 11671183.
    18. 18)
      • 18. Gomez-Clapers, J., Serrano-Finetti, E., Casanella, R., et al: ‘Can driven-right-leg circuits increase interference in ECG amplifiers?’. Annual Intl. Conf. of the IEEE EMBC, 2011, pp. 47804783.
    19. 19)
      • 19. Mahajan, R., Consul-Pacareu, S., AbuSaude, M.J., et al: ‘Ambulatory EEG NeuroMonitor platform for engagement studies of children with development delays’. SPIE Proc. Smart Biomedical and Physiological Sensor Tech X, May 2013, vol. 8719, p. 87190L(1-10).
    20. 20)
      • 20. Consul-Pacareu, S., Morshed, B.I.: ‘Power optimization of NeuroMonitor EEG device: hardware/software co-designed interrupt driven clocking’. 6th Intl. IEEE/EMBS Conf. Neural Engineering, November 2013, pp. 2528.
    21. 21)
      • 21. Mahajan, R., Majmudar, C.A., Khatun, S., et al: ‘NeuroMonitor ambulatory EEG device: comparative analysis and its application for cognitive load assessment’. IEEE Healthcare Innovations and Point-of-Care Technologies Conf., Seattle, WA, October 2014, pp. 133136.
    22. 22)
      • 22. Consul-Pacareu, S., Mahajan, R., Sahadat, M.N., et al: ‘Wearable ambulatory 2-channel EEG NeuroMonitor platform for real-life engagement monitoring based on brain activities at the prefrontal cortex’. 4th IAJC/ISAM Joint Intl. Conf., FL, September 25–27, 2014, p. 78(1-12).
    23. 23)
      • 23. Sahadat, M.N., Jacobs, E.L., Morshed, B.I.: ‘Hardware-efficient robust biometric identification from amplitude and interval features of 0.58 Second Limb (Lead I) ECG signal using logistic regression classifier’. Engineering in Medicine and Biology Society(EMBC), Chicago, IL, August 2014, pp. 14401443.
    24. 24)
      • 24. Sahadat, M.N., Consul-Pacareu, S., Morshed, B.I.: ‘Wireless ambulatory ECG signal capture for cognitive load study using the neuromonitor platform’. 6th Intl. IEEE/EMBS Conf. Neural Engineering, November 2013, pp. 497500.
    25. 25)
      • 25. Winter, B.B., Webster, J.G.: ‘Driven-right-leg circuit design’, IEEE Trans. Biomed. Eng., 1983, 30, (1), pp. 6266.
    26. 26)
      • 26. Mahajan, R., Morshed, B.I., Bidelman, G.M.: ‘Design and validation of a wearable ‘DRL-less’ EEG using a novel fully-reconfigurable architecture’. IEEE Engineering Medicine and Biology Society Conf., Orlando, FL, August 16–20, 2016, pp. 49995002.
    27. 27)
      • 27. Namgoong, W., Lerdworatawee, J.: ‘Revisiting the noise figure design metric for digital communication receiver’. Proc. Intl. Symp. Quality Electronic Design, 2003, pp. 159162.
    28. 28)
      • 28. Mahajan, R., Morshed, B.I.: ‘Sample entropy enhanced wavelet-ICA denoising technique for eye blink artifact removal from scalp EEG dataset’. 6th Intl. IEEE/EMBS Conf. Neural Engineering, 2013, pp. 13941397.
    29. 29)
      • 29. Mahajan, R., Morshed, B.I.: ‘Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA’, IEEE J. Biomed. Health Inf., 2015, 19, (1), pp. 158165.
    30. 30)
      • 30. Khatun, S., Mahajan, R., Morshed, B.I.: ‘Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG’, 2015 IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL, 2015, pp. 335340.
    31. 31)
      • 31. Mahajan, R., Morshed, B.I.: ‘Performance analysis of a DRL-less AFE for battery-powered wearable EEG measurement’, Meas. J., 2016, 90, pp. 583591.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cds.2016.0256
Loading

Related content

content/journals/10.1049/iet-cds.2016.0256
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address