EEG Operant Conditioning

Foundation and Practice of Neurofeedback for the Treatment of Epilepsy

This review provides an updated overview of the neurophysiological rationale, basic and clinical research literature, and current methods of practice pertaining to clinical neurofeedback. It is based on documented findings, rational theory, and the research and clinical experience of the authors. While considering general issues of physiology, learning principles, and methodology, it focuses on the treatment of epilepsy with sensorimotor rhythm (SMR) training, arguably the best established clinical application of EEG operant conditioning. The basic research literature provides ample data to support a very detailed model of the neural generation of SMR, as well as the most likely candidate mechanism underlying its efficacy in clinical treatment. Further, while more controlled clinical trials would be desirable, a respectable literature supports the clinical utility of this alternative treatment for epilepsy. However, the skilled practice of clinical neurofeedback requires a solid understanding of the neurophysiology underlying EEG oscillation, operant learning principles and mechanisms, as well as an in-depth appreciation of the ins and outs of the various hardware/software equipment options open to the practitioner. It is suggested that the best clinical practice includes the systematic mapping of quantitative multi-electrode EEG measures against a normative database before and after treatment to guide the choice of treatment strategy and document progress towards EEG normalization. We conclude that the research literature reviewed in this article justifies the assertion that neurofeedback treatment of epilepsy/seizure disorders constitutes a well-founded and viable alternative to anticonvulsant pharmacotherapy.

An open clinical trial utilizing real-time EEG operant conditioning as an adjunctive therapy in the treatment of crack cocaine dependence

This study investigated the treatment outcome of males dependent on crack cocaine participating in an inpatient treatment facility in which electroencephalographic operant conditioning training (EEG-OC) was added to the treatment protocol. Eighty-seven men were assessed twelve months after completion of the EEG portion of the program. Follow-up procedures of urinalyses, self-report measures, length of residence, and scores on a measure of depression were obtained and showed significant changes after treatment. The addition of EEG-OC to crack cocaine treatment regimens may promise to be an efficfive intervention for treating crack cocaine abuse and increasing treatment retention

Basic Concepts and Clinical Findings in the Treatment of Seizure Disorders with EEG Operant Conditioning

EEG Operant Conditioning (Biofeedback) and Traumatic Brain Injury

Self regulation of electrocortical activity in schizophrenia and schizotypy: a review

Contrary to the belief that schizophrenic patients will be unable to learn self control of electrocortical activity due to attentional and motivational deficits, the two studies which have investigated this, both involving operant conditioning of slow cortical potentials, have demonstrated that self regulation can take place. This was particularly true of a study of interhemispheric control. Learning difficulties were found to be more to do with sustaining motivation towards the end of sessions or training programs, rather than in initial learning. Schizotypical features in the normal population have in the case of anhedonia been associated with slower learning, while withdrawn introversion has been associated with faster learning. In view of the affirmative evidence and advances in understanding the functional significance of electroencephalographic (EEG) rhythms, the undertaking of therepeutic regimens with electrocortical operant conditioning is warranted in the schizophrenia spectrum.

Quantitative analysis of training, sleep EEG and clinical response to EEG operant conditioning in epileptics

This report is a follow-up to a previous paper which described seizure rate changes with central cortical EEG feedback training in 8 poorly controlled epileptic subjects. Data examined here include associated training compliance and performance, sleep EEG spectra, clinical EEG and anticonvulsant blood levels. The study employed a double-crossover, single-blind ABA design applied to two subgroups of epileptic patients. Both groups had in common two training periods (A1, A2) in which either 12–15 c/sec (subgroup I, n = 4) or 18–23 c/sec (subgroup II, n = 4) was reinforced in the absence of 6–9 c/sec, movement or epileptiform discharge, and one training period (B) in which 6–9 c/sec was reinforced in the absence of 12–15 or 18–23 c/sec as well as movement and epileptiform discharge. Training periods occurred primarily in the home and lasted 3 months. Compliance with training instructions and response acquisition were demonstrated. Overall anticonvulsant blood levels were low and unrelated to EEG or seizure changes. Clinical EEG findings corresponded to sleep EEG and seizure rate outcomes. Power spectral analysis of sampled non-REM sleep from all-night EEG recordings obtained after each training phase indicated contingency specific changes which were limited to sensorimotor recordings in subgroup I and corresponded to the pattern of seizure rate changes in this group. EEG changes were also limited to sensorimotor cortex in subgroup II, but were linear and paralleled a progressive decrease in seizure rate. Both groups, however, showed the same pattern of EEG changes with seizure reductions; low and high frequencies were reduced and intermediate, rhythmic frequencies increased. Correlational analysis confirmed this relationship. The pattern, duration and topographic specificity of these changes suggested a normalization of sensorimotor EEG substrates related to the EEG feedback training. Ce travail est la continuation d'un travail antérieur qui décrivait les modifications du taux de crises, dues à l'apprentissage EEG par bio-feedback chez 8 sujets épileptiques mal contrôlés. Les données examinées ici incluent l'acquiescement à l'apprentissage et la performance, les spectres EEG de sommeil, l'EEG clinique et les taux sanguins d'anti-convulsivants. Cette étude utilise un protocole ABA, double croisement, simple aveugle, appliquéà deux sous-groupes de patients épileptiques. Ces deux sous-groupes ont en commun deux périodes d'apprentissage (A1, A2) dans lesquelles soit les fréquences de 12–15 c/sec (sous-groupe 1, n = 4) soit les fréquences de 18–23 c/sec (sous-groupe 2, n = 4) ont été renforcées en l'absence de fréquences de 6–9 c/sec, de mouvements ou de décharges épileptiformes, et une période d'apprentissage (B) dans laquelle les fréquences de 6–9 c/sec ont été renforcées en l'absence des fréquences de 12–15 ou de 18–23 c/sec ainsi qu'en l'absence de mouvements et de décharges épileptiformes. Les périodes d'apprentissage sont effectuées essentiellement à la maison et durent 3 mois. L'acquiescement aux instructions d'apprentissage et l'acquisition des réponses ont été démontrés. Les taux sanguins d'anti-convulsivants sont en général bas et sans liaison avec les modifications EEG ou les crises. Les données EEG cliniques correspondent à l'EEG de sommeil at aux taux de crises. L'analyse de puissance spectrale de sommeil NREM échantillonné sur les enregistrements EEG de toute la nuit obtenus après chaque phase d'apprentissage indique des modifications contingentes spécifiques qui sont limitées aux enregistrements sensorimoteurs dans le sous-groupe 1 et correspondent aux modes de variation du taux de crises dans ce groupe. Les modifications EEG sont également limitées au cortex sensorimoteur dans le sous-groupe 2, mais sont linéaires et parallèles à la diminution progressive du taux de crises. Les deux groupes toutefois montrent le même mode de changement EEG avec la réduction des crises; les fréquences élevées et basses sont réduites et des fréquences rythmiques intermédiaires augmentent. L'analyse de corrélation confirme cette relation. La spécificité de modalité, de durée et de topographie de ces modifications suggère une normalisation du substrat sensorimoteur EEG lié à l'entraînement EEG par biofeedback.