Enhancing Extraversion Classification with Sample Entropy: a Comparison of Two EEG Epoch Lengths

With the advancement of technology, many researchers have begun to employ electroencephalography (EEG) to assess extraversion personality traits, replacing subjective self-report questionnaires. However, most EEG studies are time-consuming and have inadequate classification accuracy. Thus, this letter proposes a framework for extraversion classification using sample entropy features extracted from resting-state EEG signals. The proposed framework compares two different EEG epoch lengths (15 seconds and 120 seconds) and evaluates their impact on classification performance. To enhance the classification performance, a sequential forward selection (SFS) method is applied to ensure that only the most optimal features are utilized. Using support vector machine (SVM), k-nearest neighbors (kNN), random forest (RF), and extreme gradient boosting (XGBoost) as classifiers, the study shows that sample entropy outperforms power and coherence features in classifying extraversion. Remarkably, the framework achieves 100% classification accuracy using a single feature: the sample entropy from a 15-second eyes-open condition at the Fpz electrode. By reducing the number of required features to just one and focusing on a shorter EEG epoch length, this finding reflects the potential of developing EEG-based sensor systems that are more practical and cost-effective in the future.