Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)

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EEG Signal Classification: From Brain Activity to Text Representation

Authors
R. Sravanth Kumar1, *, R. S. Pavithra1, A. Mallikarjuna Reddy1, A. Udaya Kumar1, Victor Daniel1, Varsha Ranjalkar2
1Department of Artificial Intelligence, School of Engineering, Anurag University, Hyderabad, Telangana, India
2Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
*Corresponding author. Email: srava2010@gmail.com
Corresponding Author
R. Sravanth Kumar
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-628-9_30How to use a DOI?
Keywords
Electroencephalogram (EEG); Brain–Computer Interface (BCI); Signal Processing; Feature Extraction; Random Forest; Deep Learning; Natural Language Processing (NLP); Emotion Recognition; Brain-to-Text
Abstract

Electroencephalography (EEG) is a non invasive technique for recording the scalp potentials that reflects the synchronous neural activity. This study explores a complete EEG to text framework that spans the data acquisition, preprocessing, feature extraction, supervised classification and for the symbolic natural language mapping. Using a public EEG emotion dataset, a Random Forest classifier achieved 87% accuracy in distinguishing between the three affective states (Positive, Neutral, Negative). Each predicted state is then represented by a short text templates to demonstrate the brain-to-text conversion. The results confirm that the time frequency features enable the meaningful decoding of emotional intent, while highlighting the limitations of current symbolic text generation. The Future directions include integration with a neural language models for context aware EEG-to-text generation.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
Series
Advances in Engineering Research
Publication Date
31 March 2026
ISBN
978-94-6239-628-9
ISSN
2352-5401
DOI
10.2991/978-94-6239-628-9_30How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - R. Sravanth Kumar
AU  - R. S. Pavithra
AU  - A. Mallikarjuna Reddy
AU  - A. Udaya Kumar
AU  - Victor Daniel
AU  - Varsha Ranjalkar
PY  - 2026
DA  - 2026/03/31
TI  - EEG Signal Classification: From Brain Activity to Text Representation
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 331
EP  - 341
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_30
DO  - 10.2991/978-94-6239-628-9_30
ID  - Kumar2026
ER  -