Treffer: Computational intelligence for emotion recognition in autism spectrum disorder: a systematic review of signal-based modeling, simulation, and clinical potential.

Emotion recognition systems based on computational intelligence are emerging as powerful tools for modeling and interpreting complex emotional responses, particularly in clinical populations such as individuals with Autism Spectrum Disorder (ASD). ASD is associated with atypical patterns of emotional perception and expression, which significantly impact social functioning and quality of life. This systematic review examines recent advancements in biomedical signal-based emotion recognition systems tailored to ASD, with a focus on methods that utilize computational intelligence to analyze physiological (e.g., EEG, ECG, galvanic skin response, eye tracking) and behavioral (e.g., facial expressions, vocal cues) signals. Following PRISMA guidelines, 68 peer-reviewed studies were analyzed to assess modeling strategies, signal acquisition techniques, and simulation frameworks aimed at supporting diagnosis, monitoring, and therapeutic intervention. Key challenges identified include limited model generalizability due to reliance on neurotypical datasets, a lack of ASD-specific multimodal signal resources, and difficulties in real-time clinical deployment. The review highlights promising directions such as adaptive multimodal fusion, human-in-the-loop learning, and integration of computational simulations into clinical workflows. By bridging engineering and clinical science, this work supports the development of intelligent emotion recognition systems for real-world applications in ASD diagnosis, behavior monitoring, and personalized intervention. [ABSTRACT FROM AUTHOR]