Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
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.
