Biofeedback and Self-regulation

, Volume 1, Issue 1, pp 77–104 | Cite as

Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm

  • Joel F. Lubar
  • W. W. Bahler


Eight severely epileptic patients, four males and four females, ranging in age from 10 to 29 years, were trained to increase 12–14 Hz EEG activity from the regions overlying the Rolandic area. This activity, the sensorimotor rhythm(SMR), has been hypothesized to be related to motor inhibitory processes(Sterman, 1974). The patients represented a crosssection of several different types of epilepsy, including grand mal, myoclonic, akinetic, focal, and psychomotor types. Three of them had varying degrees of mental retardation. SMR was detected by a combination of an analog filtering system and digital processing. Feedback, both auditory and/or visual, was provided whenever one-half second of 12–14-Hz activity was detected in the EEG. Patients were provided with additional feedback keyed by the output of a 4–7-Hz filter which indicated the presence of epileptiform spike activity, slow waves, or movement. Feedback for SMR was inhibited whenever slow-wave activity spikes or movement was also present. During the treatment period most of the patients showed varying degrees of improvement. Two of the patients who had been severely epileptic, having multiple seizures per week, have been seizure free for periods of up to 1 month. Other patients have developed the ability to block many of their seizures. Seizure intensity and duration have also decreased. Furthermore, the successful patients demonstrated an increase in the amount of SMR and an increase in amplitude of SMR during the training period. Spectral analyses for the EEGs were performed periodically. The effectiveness of SMR conditioning for the control of epileptic seizures is evaluated in terms of patient characteristics and type of seizures.


Slow Wave Epileptic Seizure Activity Spike Epileptic Patient Digital Processing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Plenum Publishing Corporation 1976

Authors and Affiliations

  • Joel F. Lubar
    • 1
  • W. W. Bahler
    • 1
  1. 1.University of TennesseeUSA

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