Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.04132 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916600889737216 |
|---|---|
| author | Duraisamy, Saravanakumar Dubiel, Mateusz Rekrut, Maurice Leiva, Luis A. |
| author_facet | Duraisamy, Saravanakumar Dubiel, Mateusz Rekrut, Maurice Leiva, Luis A. |
| contents | Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt speech data to covert speech classification. We used electroencephalogram (EEG) features derived from the Hilbert envelope and temporal fine structure, and used them to train a bidirectional long-short-term memory (BiLSTM) model for classification. Our method reduces the burden of extensive training and achieves state-of-the-art classification accuracy: 86.44% for overt speech and 79.82% for covert speech using the overt speech classifier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure Duraisamy, Saravanakumar Dubiel, Mateusz Rekrut, Maurice Leiva, Luis A. Machine Learning Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt speech data to covert speech classification. We used electroencephalogram (EEG) features derived from the Hilbert envelope and temporal fine structure, and used them to train a bidirectional long-short-term memory (BiLSTM) model for classification. Our method reduces the burden of extensive training and achieves state-of-the-art classification accuracy: 86.44% for overt speech and 79.82% for covert speech using the overt speech classifier. |
| title | Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.04132 |