A Novel Spatial Fractional-Domain Approach for Short Emotion-Evoked EEG Identity Recognition

Electroencephalography (EEG) is a crucial physiological signal that reflects real-time brain activity and exhibits inherent individual variability, making it a promising modality for identity recognition. However, the nonstationarity and complexity of EEG signals make it difficult for traditional feature extraction methods to achieve efficient signal characterization in identity recognition. To address these issues, we propose a novel spatial fractional-domain (SFD) feature extraction algorithm that retains critical spatial information through common spatial pattern (CSP) while leveraging the fractional Fourier transform (FRFT) to capture both temporal and frequency characteristics. The fractional-domain allows for flexible representation of signal features by adjusting the transformation order, thereby improving the algorithm’s adaptability to the nonstationary nature of EEG signals. In addition, we have curated a new short-term speech-induced EEG dataset focusing on four primary emotions (happiness, sadness, anger, and surprise), alongside simultaneous speech signal recordings to monitor the concentration status of the subjects. Experimental results demonstrate that the proposed method achieves optimal identity recognition performance, with an accuracy peak at a fractional order of 0.1 for this dataset. Furthermore, validations on widely recognized public datasets SEED, DEAP, and FACED, showed a classification accuracy peak at fractional orders of 0.2, 0.4, and 0.1, respectively, further validating the generalizability and robustness of the SFD algorithm. These findings underscore the algorithm’s effectiveness in addressing the complexities of EEG-based identity recognition.