Deep Learning Assisted Biofeedback

Abstract

After 60 years of brain waves biofeedback development, basic and applied research, therapeutics, and a variety of devices built, there are a well-defined set of applications both, in health and illness. During these years, advances in technology made big contributions to biofeedback therapeutic and training procedures development. Variability as a natural property in biological systems and a side effect of the limitations in actual biofeedback devices along with differences in treatments and training models, have placed regular practice in a landscape where outcome prediction is difficult, not always reliable, or replicable, and with lack of fundamentals for generalization. This chapter discusses the develop of Deep Learning (DL) solutions designed to control the biofeedback process. Aim is to substitute current devices and neurofeedback procedures with a robust set of DL options designed to reduce variability and deliver biofeedback process according to the natural brain waves relations and principles, proposing DL models oriented to fill the actual vacuum of precision in current neurofeedback (NFB) devices and practice.