The use of an affect-based music selection algorithm and embedded auditory beat stimulation as an intervention for high-anxiety populations

Chronic anxiety is a growing psychological disorder worldwide and in Canada. Even when anxiety presents at pre-clinical levels, it can be disabling. Anti-anxiety drugs have many adverse side-effects. In some cases, listening to music decreases anxiety more effectively than anti-anxiety drugs. Furthermore, some evidence suggests that curating a music selection as a function of the listener’s current affective state allows for more effective and sustained changes in mood. Auditory beat stimulation (ABS) may also reduce anxiety and promote relaxation. This research will examine the contexts within which and the extent to which an affect-based music recommendation system in combination with ABS reduces anxiety symptom severity. Electroencephalographic (EEG) frequency-domain measures, galvanic skin response (GSR), Heart Rate Variability (HRV), anxiety measures, and emotional state measures will be used. This research is being conducted with the aim of further developing and validating this noninvasive audio-based anxiety treatment modality.

Faculty Supervisor:

Frank Russo

Student:

Adiel Mallik

Partner:

LUCID

Discipline:

Psychology

Sector:

Professional, scientific and technical services

University:

Ryerson University