Automatic optimal-reward-frequency selection by arousal estimation and optimization for the enhancement of ILF neurofeedback treatments (ORFARO)

Funding type: unibz internal funds

Duration: 01/12/2022 – 30/11/2025

During the COVID-19 pandemic many people reported worsened symptoms of mental or somatoform disorders or found themselves confronted with the Long COVID-19 or post-acute COVID-19 syndrome. In this proposal, we aim at advancing neurofeedback treatment methods to achieve a more effective treatment of persons with mental or somatoform disorders. Neurofeedback is considered a method to treat symptoms caused by functional anomalies in the central nervous system and has gained increasing popularity as alternative or in combination with classical treatments of e.g. epilepsy, ADHS as well as learning, sleeping, and anxiety disorders. From the larger set of developed methods, the so-called infra-low-frequency (ILF) neurofeedback method or Othmer method gained significant importance recently. One of the key challenges for this therapy to be effective though is a proper selection of the optimal reward frequency of the provided neurofeedback as its selection requires a lot of experience of the therapist in the estimation of the patient’s arousal level based on observations, question-answer sessions as well as physiological recordings. Thus, in this project we aim at developing an extension to the existing neurofeedback system that supports the therapist in the selection process of the ORF and with this improves and accelerates the progress of the therapy of mental or somatoform disorders like the ones occuring after a COVID-19 infection. This should be achieved by: i) online estimation of the emotion (arousal state) from EEG and physiological signals, ii) development of an arousal controller that gradually drives the subject’s arousal to the desired level by automatically adjusting the ORF, and a final iii) comparison of the effectiveness of the resulting therapy based on automatic ORF adaptation to a therapy based on manual adaptations made by the therapist. The proposed research is highly interdisciplinary and requires the integration of knowledge from fields like emotion modelling and control as well as clinical neuropsychology and may pave the way for a more efficient, computer-assisted neurofeedback therapy, which in future may even include multiple, simultaneous electrode positions. Further, the research is able to provide valuable insights into processes involved in ILF neurofeedback therapy, as well as much needed empirical evidence of the therapy outcome, which is still in its infancy.

Photography: credits NOI Techpark/Daniele Fiorentino