Decoding Olfactory EEG Signals Using Multi-Domain Features and Machine Learning

Accurate detection of human emotion is an important topic for affective computing. Especially with the rise of artificial intelligence in the marketing industry, the tools available are subjective and often heavily dependent on sample sizes and demographics. This study explores the neural responses to olfactory stimuli by analyzing EEG data collected from 57 participants exposed to a perfume scent in correlation with self-reported survey results. The electroencephalogram (EEG) signals were processed to extract time-domain, spectral-domain, and nonlinear features, which were subsequently classified using various machine learning algorithms. The classification outcomes were mapped onto a two-dimensional pleasure-arousal plane, with the Medium Gaussian support vector machine (SVM) achieving the highest performance, including $99.8 \%$ validation accuracy and $100 \%$ test accuracy. These results highlight the significant potential of EEG-based approaches in decoding the neural underpinnings of sensory experiences, with implications for applications in neuromarketing and therapeutic contexts.