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This study classifies and estimates the intensity of multiple emotional states using physiological signals. We employed a jigsaw puzzle task to elicit both positive and negative emotions in participants. Mood states were assessed using the profile of mood states 2nd Edition (POMS2), while electroencephalogram (EEG) and heart rate variability (HRV) signals were recorded simultaneously. Support vector machines (SVMs) were used for emotion classification. Feature extraction techniques were applied to enhance classification accuracy, including principal component analysis (PCA) and autoencoders (AE). Recursive feature elimination (RFE) was utilized to identify key physiological indicators. When PCA or AE preprocessing was applied, the classification model achieved a κ coefficient of over 0.9 for all emotions. The key features for emotion classification were identified as mean RR interval (MRRI), low-frequency power (LF), high-frequency power (HF), ratio, and prefrontal alpha asymmetry (Fp1α-Fp2α), whereas HF, standard deviation of RR intervals, LF, and F7α-F8α showed lower importance. The findings suggest that EEG and HRV signals can classify and estimate multiple emotional states simultaneously. These results contribute to developing objective emotion recognition systems for applications in mental health monitoring and affective computing.Clinical Relevance- Accurately assessing emotional states is crucial for mental health care, stress management, and affective computing applications. The proposed emotion classification model utilizing EEG and HRV signals provides an objective and quantitative approach to evaluating mood states. This study demonstrates the feasibility of non-invasive physiological monitoring for mental well-being assessment, offering potential applications in workplace stress management, early detection of mood disorders, and human-computer interaction systems.