The Data-Driven Decision Making (D3M) Lab is pleased to celebrate the publication of undergraduate researcher Yeganeh’s research paper, “Systematic Literature Review of Machine Learning Methods for Emotion Recognition Using EEG and Physiological Signals in Healthcare,” published through IEEE Access. This achievement represents the culmination of approximately 18 months of dedicated research, literature analysis, writing, revision, and scholarly collaboration.
Publication: Systematic Literature Review of Machine Learning Methods for Emotion Recognition Using EEG and Physiological Signals in Healthcare
IEEE Xplore: https://ieeexplore.ieee.org/document/11475400
The study provides a comprehensive review of machine learning and artificial intelligence approaches used for emotion recognition from electroencephalography (EEG) and other physiological signals in healthcare settings. As interest in affective computing and human-centered AI continues to grow, understanding how computational models can detect emotional states has become increasingly important for applications including mental health assessment, patient monitoring, personalized healthcare, and assistive technologies.
Over the course of more than a year and a half, Yeganeh conducted an extensive examination of the rapidly expanding body of literature in emotion recognition research. The review synthesized findings from numerous studies, evaluated commonly used physiological sensing modalities, compared machine learning and deep learning approaches, and identified current challenges and future opportunities for AI-driven emotion analysis in healthcare.
This publication highlights the growing impact of undergraduate research within the D3M Lab and demonstrates the value of long-term student engagement in scholarly research. Through this project, Yeganeh gained extensive experience in systematic literature review methodologies, scientific writing, critical analysis, and academic publishing.
The D3M Lab remains committed to providing undergraduate researchers with opportunities to contribute to meaningful scientific discoveries in artificial intelligence, machine learning, healthcare analytics, biomedical informatics, and data-driven decision-making.