An Explainable Feature Engineering Model Based on Automata Pattern: Investigations on the EEG Artifact Classification

Abstract

We introduce Automata Pattern (AutPat), a feature extractor for EEG, and embed it in an explainable feature engineering (XFE) pipeline. We evaluated AutPat on three tasks: EEG artifact classification, stress detection, and mental performance detection. The pipeline computes AutPat features from raw EEG, selects informative variables with cumulative weighted iterative neighborhood component analysis (CWINCA), and performs classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. For interpretability, we map the selected features to Directed Lobish (DLob) symbols and derive DLob strings and cortical connectome diagrams. The AutPat-based XFE achieved > 88% classification accuracy on all datasets. CWINCA reduced the feature space while maintaining accuracy, and the DLob layer yielded dataset-specific symbolic outputs and 8 x 8 connectome matrices. AutPat, combined with CWINCA and tkNN, provides a compact and accurate EEG pipeline with inherent symbolic explanations. The results indicate that AutPat-based XFE is a practical option for EEG analysis when both performance and interpretability are required.