Skip to main content
Log in

EEG-based system for rapid on-off switching without prior learning

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

Details are reported of an EEG-based system that permits a person rapidly and reliably to switch on and off electrical devices without prior learning. The system detects and utilises increases in the amplitude of the alpha component of the EEG spectrum that occur when people close their eyes for more than 1 s. In addition to conventional signal-processing elements, the system incorporates a module for suppressing switching at the output of the system when predetermined noise threshold levels (such as those due to sources of EMG) are exceeded. This work indicates that a majority, perhaps in excess of 90%, of the adult population can demonstrate the control necessary to operate an electrical device or appliance using this system. It is indicated that multilevel switching and quasi-continuous control options are feasible with further development of the system. This work has implications for the design of a system that could be used, for example, to assist the infirm or severely physically disabled to effect greater control over their environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andreassi, J. L. (1995): ‘Psychophysiology: human behaviour and physiological response, 3rd edn’. (Lawrence Erlbaum & Associates, New Jersey) p. 23

    Google Scholar 

  • Aston, R. (1990): ‘Principles of biomedical instrumentation and measurement’, (Merrill, Columbus)

    Google Scholar 

  • Barlow, J. (1984): ‘EMG artefact minimization during clinical EEG recordings by special analog filtering’,Electroenceph. Clin. Neurophys.,58 pp. 161–174

    Article  Google Scholar 

  • Blanchard, E.B., Nicolson, N.L., Radnitz, C.L., Steffek, B.D., Appelbaum, K.A., andDentinger, M.P. (1991): ‘The role of home practice in thermal biofeedback’,J. Consult. Clin. Psych.,59, pp. 507–512

    Article  Google Scholar 

  • Craig, A. R., Hancock, K., andDickson H. (1994): ‘A longitudinal investigation into anxiety and depression in the first 2 years following spinal cord injury’,Paraplegia,32, pp. 675–679

    Google Scholar 

  • Fisch, B. J. (1991): ‘Spehlmann's EEG primer, 2nd edn’, (Elsevier, New York) chap. 13

    Google Scholar 

  • Ford, M., Bird, B. L., Newton, F. A., andSheer, D. (1980): ‘Maintenance and generalization of 40 Hz EEG biofeedback effects’,Biofeedback Self-Regul.,5, pp. 193–205

    Article  Google Scholar 

  • Furedy, J.T., andShulhan, D. (1987): ‘Specific versus placebo effects in biofeedback; Some brief back to basic considerations’,Biofeedback Self-Regul.,12, pp. 211–215

    Article  Google Scholar 

  • Geddes, L. A., andBaker, L. E. (1989): ‘Principles of applied biomedical instrumentation, 3rd edn’, (Wiley, New York) pp. 721–723

    Google Scholar 

  • Hare, J.F., Timmons, B.H., Roberts, J.R., andBurman, A.S. (1982): ‘EEG alpha-biofeedback training: an experimental technique for the management of anxiety’,J. Med. Eng. Tech.,6, pp. 19–24

    Google Scholar 

  • Hogan, N. (1976) ‘A review of the methods of processing EMG for use as a proportional control signal’,Bio-Med. Eng.,11, (3) pp. 81–6

    Google Scholar 

  • Keirn, Z. A., andAunon J. I. (1990): ‘A new mode of communication between man and his surroundings,’IEEE Trans.,BME-37, pp. 1209–1214

    Google Scholar 

  • LaCourse, J. R., andHudlik, F. C. Jr. (1990): ‘An eye movement communication-control system for the disabled’,IEEE Trans.,BME-37, pp. 1215–1220

    Google Scholar 

  • Mulholland, T. B., Boudrot, R., andDavidson, A. (1979): ‘Feedback delay and amplitude threshold and control of occipital EEG’,Biofeedback Self-Regul.,4, pp. 93–107

    Article  Google Scholar 

  • Pfurtscheller, G., Flotzinger, D., andKalcher, J. (1993): ‘Brain-Computer Interface: a new communication device for handicapped persons’,J. Microcomput. Appl.,16, pp. 293–299

    Article  Google Scholar 

  • Sadasivan, P. K., andDutt, D. N. (1994): ‘Minimization of EOG artefacts from corrupted EEG signals using a neural network approach’,Comput. Biol. Med.,24, pp. 441–449

    Article  Google Scholar 

  • Sadasivan, P. K., andDutt, D.N. (1995): ‘Use of finite wordlength FIR digital filter structures with improved magnitude and phase characteristics for reduction of muscle noise in EEG signals’,Med. Biol. Eng. Comput.,33, pp. 306–312

    Article  Google Scholar 

  • Somers, V. K., Dyken, M. E., Clary, M. P., andAbboud, F. M. (1995): ‘Sympathetic neural mechanisms in obstructive sleep apnea’,J. Clin. Invest.,94, pp. 1897–1904

    Article  Google Scholar 

  • Wolpaw, J. R., McFarland, D. J., Neat, G. W., andForneris, C. A. (1991): ‘An EEG-based brain-computer interface for cursor control’,Electroenceph. Clin. Neurophys.,78, pp. 252–259

    Article  Google Scholar 

  • Yana, K., Fukuda, M., andTakano, N. (1987): ‘EOG cancelling in EEG: An adaptive filter approach for ERP estimation’, IEEE Ninth Conf. of Eng. in med. & biol., pp. 860–861

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. Kirkup.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kirkup, L., Searle, A., Craig, A. et al. EEG-based system for rapid on-off switching without prior learning. Med. Biol. Eng. Comput. 35, 504–509 (1997). https://doi.org/10.1007/BF02525531

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02525531

Keywords

Navigation