Machine Learning and Validity of Binaural Beat Protocols: Trainability and Interpretability

Statement of the Problem: Binaural beat (BB) is a form of sound wave therapy in which both ears received sounds of slightly different frequencies, yet auditory cortex perceived as a single signal [1]. BB therapy is provided in frequency ranges corresponding to electroencephalogram (EEG) bands (theta, alpha, beta, and gamma). Studies have shown benefits of different types of BB therapy for treatment of anxiety, depression, mood, and memory [1-4]. Studies used different cognitive and EEG tests for studying psychological and neurological changes following BB stimulation [3, 4]. However, the unknown mechanism of BB therapy is a challenge for end users to implement BB in clinics [5]. The unknown mechanism might be due to lack in validation processes. In this study, machine learning and regression analysis was applied to study the neurological and psychological changes.


Muhammad Abul Hasan

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