Training in EEG Neurofeedback: Predicting Successful Treatment

EEG neurofeedback treatment (NFT) has proved useful in the rehabilitation and treatment of many disorders including autism, attention disorders, epilepsy, traumatic-brain injury, and dyslexia, among others, and it is currently being explored for use in treatment of language disorders. However, multiple studies have shown the inability of some patients to respond to treatment, and there is currently no accepted explanation for this phenomenon. This highlights the importance of developing methods to assess individualized treatment response, and ways to better understand the factors that may lead to treatment inefficacy in some patients. This paper reviews the recent research on neurofeedback treatment, focusing on predictors for both successful and unsuccessful treatment outcomes such as the current state of the patient, various traits of the patient, and varying neurofeedback methods. This literature is discussed in the context of an ongoing study at Baylor focused on developing prospective assessments which estimate the likelihood of NFT treatment success before beginning therapy. By understanding the mechanisms of successful brain regulation through neurofeedback training and adapting treatment to each patient, significant changes in a patient’s quality of life can be observed. Furthermore, increasing the efficacy of neurofeedback can have lasting implications for patients with cognitive and language disorders, through reducing symptoms, and enhancing overall intellectual and social performance.