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...
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| Vydáno v: | The ... International Winter Conference on Brain-Computer Interface s. 1 - 4 |
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24.02.2025
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| ISSN: | 2572-7672 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Kim, Jun-Young Lee, Seo-Hyun Ko, Byung-Kwan |
| Author_xml | – sequence: 1 givenname: Byung-Kwan surname: Ko fullname: Ko, Byung-Kwan email: leaderbk525@korea.ac.kr organization: Korea University,Dept. of Artificial Intelligence,Seoul,Republic of Korea – sequence: 2 givenname: Jun-Young surname: Kim fullname: Kim, Jun-Young email: j_y_kim@korea.ac.kr organization: Korea University,Dept. of Artificial Intelligence,Seoul,Republic of Korea – sequence: 3 givenname: Seo-Hyun surname: Lee fullname: Lee, Seo-Hyun email: seohyunlee@korea.ac.kr organization: Korea University,Dept. of Brain and Cognitive Engineering,Seoul,Republic of Korea |
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| Snippet | This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using... |
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| SubjectTerms | 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 |
| Title | Imagined Speech State Classification for Robust Brain-Computer Interface |
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