Imagined Speech State Classification for Robust Brain-Computer Interface

This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep...

Full description

Saved in:
Bibliographic Details
Published in:The ... International Winter Conference on Brain-Computer Interface pp. 1 - 4
Main Authors: Ko, Byung-Kwan, Kim, Jun-Young, Lee, Seo-Hyun
Format: Conference Proceeding
Language:English
Published: IEEE 24.02.2025
Subjects:
Accuracy
Brain modeling
brain-computer interface
Brain-computer interfaces
Data models
Deep learning
Electroencephalography
Feature extraction
imagined speech
machine learning
Reliability
signal processing
Speech enhancement
Training
ISSN:2572-7672
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep learning architectures such as EEGNet, ShallowConvNet, and DeepConvNet. Machine learning classifiers exhibited significantly lower precision and recall, indicating limited feature extraction capabilities and poor generalization between imagined speech and idle states. In contrast, deep learning models, particularly EEGNet, achieved the highest accuracy of 0.7080 and an F1 score of 0.6718, demonstrating their enhanced ability in automatic feature extraction and representation learning, essential for capturing complex neurophysiological patterns. These findings highlight the limitations of conventional machine learning approaches in brain-computer interface (BCI) applications and advocate for adopting deep learning methodologies to achieve more precise and reliable classification of detecting imagined speech. This foundational research contributes to the development of imagined speech-based BCI systems.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931499