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Title A DEEP LEARNING METHOD FOR INNER SPEECH CLASSIFICATION USING EEG SIGNAL

Author(s) Muhammad Ameer Hamza

Abstract This thesis studies utilizing electroencephalography (EEG) signals the difficulties of subject-independent inner speech classification. Particularly for those with severe motor disabilities, inner speech the act of silently communicating to oneself offers a potential modality for Brain-Computer Interfaces (BCIs). Low signal-to-noise ratios and great inter-subject variability make deciphering inner speech from EEG difficult, nevertheless. Based on EEG data, this thesis evaluates several machine learning and deep learning models for inner speech classification. Particularly, a deep learning model, a Convolutional Neural Network (CNN) with triplet loss, is contrasted against conventional machine learning methods including Linear Support Vector Machine (SVM), More general SVM with various kernels, and LightGBM. Subject-independent framework with leave-one-subject-out cross-valuation on the Thinking Out Loud (TOL) dataset evaluates the models. Performance is evaluated with reference to accuracy, F1-score, precision, and recall. The CNN-based triplet network achieves the best average accuracy among other models, so the results show the promise of deep learning for subject-independent inner speech classification. Although the results imply that deep learning presents a viable path for future research, especially with bigger and more diverse datasets and advanced architectures, benefits over conventional approaches are minor. This work advances knowledge of the difficulties and possible solutions for creating strong, generally applicable inner voice BCIs.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

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