ELECTROENCEPHALOGRAPHY CHANGES IN EMOTIONAL RECOGNITION: A SIGNAL PROCESSING APPROACH USING WAVELET FEATURES

Abstract: Introduction: Emotion recognition using electroencephalography
(EEG) has gained significant attention in the fields of affective computing and
human–computer interaction due to its ability to capture real-time neural
responses. This study aims to analyze EEG signal variations associated with
emotional states using a wavelet-based signal processing approach within the
arousal–valence framework.
Methods: A prospective observational study was conducted on 48 healthy
participants. Emotional states were induced using image, video, and music
stimuli categorized under the arousal–valence model. EEG signals were
recorded using the standard 10–20 electrode placement system. The
acquired signals were pre-processed and analysed using Discrete Wavelet
Transform (DWT) to extract time–frequency features, including energy and
entropy. Statistical analysis was performed to evaluate hemispheric
differences and gender-based variations.
Results: Significant differences in EEG activity were observed between the
left and right hemispheres across multiple emotional states (p < 0.05),
confirming hemispheric asymmetry in emotional processing. Video and music
stimuli produced stronger and more distinguishable EEG responses
compared to image-based stimuli. Negative emotions demonstrated higher
discriminative patterns than positive emotions. Gender-based variations were
also observed in EEG responses.
Conclusion: The study demonstrates that wavelet-based EEG feature
analysis provides meaningful and discriminative information for emotion
recognition. The findings highlight the potential of EEG signals in developing
automated emotion recognition systems and contribute to advancements in
brain–computer interface (BCI) and affective computing applications.