Quantifying the Feasibility of Compressive Sensing in Portable Electroencephalography Systems

  • Amir M. Abdulghani
  • Alexander J. Casson
  • Esther Rodriguez-Villegas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


The EEG for use in augmented cognition produces large amounts of compressible data from multiple electrodes mounted on the scalp. This huge amount of data needs to be processed, stored and transmitted and consumes large amounts of power. In turn this leads to physically large EEG units with limited lifetimes which limit the ease of use, and robustness and reliability of the recording. This work investigates the suitability of compressive sensing, a recent development in compression theory, for providing online data reduction to decrease the amount of system power required. System modeling which incorporates a review of state-of-the-art EEG suitable integrated circuits shows that compressive sensing offers no benefits when using an EEG system with only a few channels. It can, however, lead to significant power savings in situations where more than approximately 20 channels are required. This result shows that the further investigation and optimization of compressive sensing algorithms for EEG data is justified.


Compressive Sensing Electroencephalogram Power efficient Wireless Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yates, D., Casson, A., Rodríguez-Villegas, E.: Low Power Technology for Wearable Cognition Systems. In: 13th International Conference on Human-Computer Interaction, pp. 127–136 (2007)Google Scholar
  2. 2.
    Aviyente, S.: Compressive sampling Framework for EEG Compression. In: IEEE/SP 14th Workshop on Statistical Signal Processing, pp. 181–184 (2007)Google Scholar
  3. 3.
    Lustig, M., Donoho, D., Santos, J., Pauly, J.: Compressive sampling MRI (A look at how CS can improve on current imaging techniques). IEEE Signal Processing Magazine 25(2), 72–82 (2008)CrossRefGoogle Scholar
  4. 4.
    Donoho, D.: Compressive sampling. IEEE Trans. Inform. Theory 52(4), 1289–1306 (2006)CrossRefGoogle Scholar
  5. 5.
    Candès, J., Wakin, M.: People hearing without listening: an introduction to compressive sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)CrossRefGoogle Scholar
  6. 6.
    Harrison, R., Charles, C.: A low-power low-noise CMOS amplifier for neural recording applications. IEEE Journal of Solid State Circuits 38(6), 958–965 (2003)CrossRefGoogle Scholar
  7. 7.
    Yates, D., Villegas-Rodriguez, E.: An ultra low power low noise chopper amplifier for wireless EEG. In: The 49th IEEE Midwest Symposium on Circuits and Systems, pp. 449–452 (2006)Google Scholar
  8. 8.
    Menolfi, C., Huang, Q.: A Fully Integrated, Untrimmed CMOS instrumentation amplifier with sub microvolt offset. IEEE Journal of Solid State Circuits 34(3), 415–420 (1999)CrossRefGoogle Scholar
  9. 9.
    Steyaert, M., Sansen, W., Zhongyuan, C.: A Micropower Low-noise monolithic instrumentation amplifier for medical purposes. IEEE Journal of Solid State Circuits 22(6), 1163–1168 (1987)CrossRefGoogle Scholar
  10. 10.
    Wu, H., Xu, Y.: A Low-Voltage Low-Noise CMOS Instrumentation Amplifier for Portable Medical Monitoring Systems. In: IEEE-NEWCAS, pp. 295–298 (2005)Google Scholar
  11. 11.
    Yazicioglu, R.F., Merken, P., Puers, R., Van Hoof, C.: A 200μW Eight-Channel EEG Acquisition ASIC for Ambulatory EEG Systems. IEEE Journal of Solid State Circuits 43(12), 3025–3038 (2008)CrossRefGoogle Scholar
  12. 12.
    Enz, C., Vittoz, E., Krummenacher, F.: A CMOS Chopper Amplifier. IEEE Journal of Solid State Circuits 22(3), 335–342 (1987)CrossRefGoogle Scholar
  13. 13.
    Nuwer, M., Combi, G., Emerson, R., Fuglsang-Frederiksen, A., Guérit, J., Hinrichs, H., Ikeda, A., Luccas, F., Rappelsburger, P.: ICFN standards for digital recording of clinical EEG. Electroencephalography and Clinical Neurophysiology 106, 259–261 (1998)CrossRefPubMedGoogle Scholar
  14. 14.
    Dlugosz, R., Gaudet, V., Iniewski, K.: Flexible Ultra Low Power Successive Approximation Analog-to-Digital Converter with Asynchronous Clock Generator. In: Canadian Conference on Electrical and Computer Engineering, pp. 1649–1652 (2007)Google Scholar
  15. 15.
    Verma, N., Chandrakasan, A.: An ultra low energy 12-bit rate-resolution scalable SAR ADC for wireless sensor nodes. IEEE Journal of Solid State Circuits 42(6), 1196–1205 (2007)CrossRefGoogle Scholar
  16. 16.
    Bonfini, G., Garbossa, C., Saletti, R.: A switched Opam-based 10-b integrated ADC for ultra low power applications, VLSI-SOC (2003)Google Scholar
  17. 17.
    Gambini, S., Rabaey, J.: A 100KS/s 65dB DR Σ — Δ ADC with 0.65V supply voltage. In: 33rd European Solid State Circuits Conference, pp. 202–205 (2007)Google Scholar
  18. 18.
    López-Morillo, E., Gonzalez-Carvajal, R., Galan, J., Ramirez-Angulo, J., Lopez-Martin, A., Rodriguez-Villegas, E.: A Low-Voltage Low-Power QFG-based Sigma-Delta Modulator for Electroencephalogram Applications. In: IEEE BioCAS, pp. 118–121 (2006)Google Scholar
  19. 19.
    Holleman, J., Otis, B., Bridges, S., Mitros, A., Diorio, C.: A 2.92μW Hardware Random Number Generator. In: ESSCIRC Solid-State Circuits Conference, pp. 134–137 (2006)Google Scholar
  20. 20.
    Kakaradov, B.: Ultra-Fast Matrix Multiplication: An Empirical Analysis of Highly Optimized Vector Algorithms. Stanford Undergraduate Research Journal 3, 33–36 (2004)Google Scholar
  21. 21.
    Beling, P., Megiddo, N.: Using fast matrix multiplication to find basic solutions. Theoretical Computer Science 205(1-2), 307–316 (1998)CrossRefGoogle Scholar
  22. 22.
    Texas Instruments: MSP430 DataSheet,
  23. 23.
    XS110 UWB solution for media-rich wireless applications,
  24. 24.
    Kahol, K., Smith, M., Mayes, S., Deka, M., Deka, V., Ferrara, J., Panchanathan, S.: The Effect of Fatigue on Cognitive and Psychomotor Skills of Surgical Residents. In: 13th International Conference on Human-Computer Interaction, pp. 304–313 (2007)Google Scholar
  25. 25.
    Stevens, R., Galloway, T., Berka, C.: Exploring Neural Trajectories of Scientific Problem Solving Skill Acquisition. In: 13th International Conference on Human-Computer Interaction, pp. 400–408 (2007)Google Scholar
  26. 26.
    Huang, R.-S., Jung, T.-P., Makeig, S.: Event-Related Brain Dynamics in Continuous Sustained-Attention Tasks. In: 13th International Conference on Human-Computer Interaction, pp. 65–74 (2007)Google Scholar
  27. 27.
    Van Orden, K., Viirre, E., Kobus, D.: Augmenting Task-Centered Design with Operator State Assessment Technologies. In: 13th International Conference on Human-Computer Interaction, pp. 212–219 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Amir M. Abdulghani
    • 1
  • Alexander J. Casson
    • 1
  • Esther Rodriguez-Villegas
    • 1
  1. 1.Circuits and Systems Group, Department of Electrical and Electronic EngineeringImperial College LondonUK

Personalised recommendations