Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data

Abstract

Classification of motor tasks is of significant interest in brain-computer interfacing today. Electroencephalograph data contains a large amount of noise obfuscating the signal associated with these motor tasks. Various preprocessing techniques exist to increase the signal-to-noise ratio allowing for more accurate classifications. The effectiveness of these techniques varies between motor tasks and in different environments. There is a need to evaluate these different techniques in many different environments and with different motor tasks. This thesis investigates the effectiveness of several preprocessing techniques and classification models for classifying four different motor imagery tasks from EEG data. Specifically, Frequency Filtering, ICA, and CSP are evaluated using Naive Bayes, kNN, Linear SVM, RBF SVM, LDA, Random Forest, and a MLP Neural Network.To control for the environment, data was collected from student volunteers in short sessions designed to demonstrate either eye blinking, eye rolling, jaw clenching, or neck turning. Each task had its own procedure for the session. Motor tasks in data were evaluated for frequency and amplitude commonalities using continuous wavelet transforms and Fourier transforms. Preprocessing Techniques were then iteratively applied to these datasets and evaluated using an ML model. The evaluation metrics used were Accuracy, F1, Precision, and Recall. Results showed that the combination of Frequency Filtering, ICA, and CSP with the Naive Bayes and Random Forest models yielded the highest accuracy and F1 for all motor tasks. These findings contribute to the field of EEG signal processing and could have potential applications in the development of brain-computer interfaces. It also directly contributes to a greater project in spatial neglect rehabilitation by providing novel insights to common artifacts in EEG data, as well as to the creation of a framework for data processing in real-time and offline.