Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913705379233792 |
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| author | Gedawy, Mostafa El Nabil, Omnia Mamdouh, Omar Nady, Mahmoud Adel, Nour Alhuda Fares, Ahmed |
| author_facet | Gedawy, Mostafa El Nabil, Omnia Mamdouh, Omar Nady, Mahmoud Adel, Nour Alhuda Fares, Ahmed |
| contents | Brain activity translation into human language delivers the capability to revolutionize machine-human interaction while providing communication support to people with speech disability. Electronic decoding reaches a certain level of achievement yet current EEG-to-text decoding methods fail to reach open vocabularies and depth of meaning and individual brain-specific variables. We introduce a special framework which changes conventional closed-vocabulary EEG-to-text decoding approaches by integrating subject-specific learning models with natural language processing methods to resolve detection obstacles. This method applies a deep representation learning approach to extract important EEG features which allow training of neural networks to create elaborate sentences that extend beyond original data content. The ZuCo dataset analysis demonstrates that research findings achieve higher BLEU, ROUGE and BERTScore performance when compared to current methods. The research proves how this framework functions as an effective approach to generate meaningful and correct texts while understanding individual brain variations. The proposed research aims to create a connection between open-vocabulary Text generation systems and human brain signal interpretation for developing efficacious brain-to-text systems. The research produces interdisciplinary effects through innovative assistive technology development and personalized communication systems which extend possibilities for human-computer interaction in various settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17465 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding Gedawy, Mostafa El Nabil, Omnia Mamdouh, Omar Nady, Mahmoud Adel, Nour Alhuda Fares, Ahmed Signal Processing Computation and Language Machine Learning Brain activity translation into human language delivers the capability to revolutionize machine-human interaction while providing communication support to people with speech disability. Electronic decoding reaches a certain level of achievement yet current EEG-to-text decoding methods fail to reach open vocabularies and depth of meaning and individual brain-specific variables. We introduce a special framework which changes conventional closed-vocabulary EEG-to-text decoding approaches by integrating subject-specific learning models with natural language processing methods to resolve detection obstacles. This method applies a deep representation learning approach to extract important EEG features which allow training of neural networks to create elaborate sentences that extend beyond original data content. The ZuCo dataset analysis demonstrates that research findings achieve higher BLEU, ROUGE and BERTScore performance when compared to current methods. The research proves how this framework functions as an effective approach to generate meaningful and correct texts while understanding individual brain variations. The proposed research aims to create a connection between open-vocabulary Text generation systems and human brain signal interpretation for developing efficacious brain-to-text systems. The research produces interdisciplinary effects through innovative assistive technology development and personalized communication systems which extend possibilities for human-computer interaction in various settings. |
| title | Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding |
| topic | Signal Processing Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2502.17465 |