Deep learning models, particularly hybrid CNN-LSTM architectures utilizing EEG and peripheral physiological signals, are the dominant approach for emotion prediction in performance-critical tasks.
Systematic Review (n=25)
This systematic review highlights that while EEG and hybrid deep learning models dominate emotion prediction in performance-critical tasks, significant challenges remain regarding real-world validity and standardized evaluation.
Emotions strongly influence how people think, decide, and perform, making reliable emotion forecasting essential in performance-critical environments. Traditional methods such as facial expressions, speech, and self-reports often lack reliability and continuity. Physiological signals offer a more objective alternative, providing continuous indicators of emotional states, while deep learning models are well-suited to capturing their non-linear temporal characteristics. Unlike prior reviews that primarily focus on general emotion recognition or isolated model performance, this study specifically examines emotion prediction in performance-critical contexts through the combined analysis of physiological signals, deep learning architectures, and task-driven requirements. This systematic review synthesizes recent studies on emotion prediction using physiological data and deep learning models. Following the PRISMA framework, relevant studies published between 2021 and 2025 were identified from the Dimensions AI and Web of Science databases, resulting in 25 eligible articles. The review examines trends in physiological modalities, deep learning architecture, emotion representations, and evaluation practices. Beyond summarizing these trends, the review provides a structured comparative synthesis that organizes existing studies according to physiological signal modality, model architecture, performance-critical task context, emotion representation, and evaluation practices, thereby offering methodological guidance for future emotion prediction system design. Findings show that EEG is the most widely used modality, frequently combined with peripheral signals such as heart rate variability, electrodermal activity and electrocardiography in multimodal systems. Hybrid architectures, particularly CNN–LSTM models, dominate current approaches, although attention-based and lightweight models are gaining traction. Key challenges remain, including inter-subject variability, limited real-world validity, inconsistent emotion modeling and non-standardized evaluation. This review highlights current gaps and offers guidance for developing more robust emotion prediction systems in high-performance contexts.
Teridi et al. (Thu,) conducted a systematic review in Emotion prediction in performance-critical tasks (n=25). Deep learning models with physiological signals was evaluated. Deep learning models, particularly hybrid CNN-LSTM architectures utilizing EEG and peripheral physiological signals, are the dominant approach for emotion prediction in performance-critical tasks.
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