Digital music interventions for stress with bio-sensing: a survey - Ferguson, Arron; Castellanos, Carlos; Pasquier, Philippe (Frontiers in Computer Science, 2023)
In an age of crisis events such as pandemics, wars, and economic turbulence, therapeutic use of music is increasingly being researched and proven to be a successful mediator for stress and anxiety. New ways of designing human-computer interaction with researchers and music therapists continues to evolve. We present a survey of therapeutic music systems that utilize biofeedback for the purpose of reducing stress and anxiety. These systems use biofeedback such as ECG and EEG which help researchers and music therapists address the psychophysiological relationships between body and mind. In our analysis of these systems, we discuss how adaptivity via machine learning can yield results that benefit music therapists and researchers alike when addressing stress and anxiety. Adaptivity such as customizing playlists, adjusting music tempo, altering the amplitude, and generating binaural beats – all based on the biofeedback from subjects. We conclude with the posit that biofeedback paired with adaptive software systems can be used to create adaptive therapeutic music system that assist researchers and music therapists in addressing stress and anxiety.