Comparative Assessment of Machine Learning Models in EEG-Based Sentence Classification

Abstract

In this study conducted a detailed analysis of widely used machine learning algorithms for sentence classification on TSEEG (Turkish EEG) dataset, examining both classification accuracy and computational efficiency. Given the complex nature of EEG signals, the study also investigates the necessity and impact of preprocessing techniques—such as normalization, filtering, and feature extraction—on classification performance. Among the models evaluated, the Support Vector Machine (SVM) achieved the highest test accuracy (99.17%) and F1 score (99.12%), demonstrating strong reliability and processing speed. To further validate the reliability and robustness of the SVM model, cross-validation was used, confirming its stability across different data subsets. This study not only emphasizes the critical role of preprocessing in enhancing EEG data analysis but also provides practical benchmarks on accuracy and processing time, offering valuable guidance for model selection in similar research. By identifying effective algorithmic choices, this work supports future research in making informed decisions regarding preprocessing and model selection across diverse EEG datasets and application domains.