Semantic EEG-to-Speech/text translation with visual feedback and large vocabulary dictionary

Home Research Projects Smart Health & Biomedical Semantic EEG-to-Speech/text translation with visual feedback and large vocabulary dictionary

Successful candidates will join the College of Engineering and Computer Science (CECS), VinUni as PhD students or Postdoctoral Research Associate.

Project Information

Translating brain signals into meaningful speech or text is one of the most promising directions in brain–computer interface (BCI) research. However, current EEG-to-speech systems still struggle with semantic accuracy, especially for large-vocabulary settings or limited training data.

This project, supervised by Prof. Sanei, introduces a novel framework that integrates visual feedback to support brain-driven word selection and emotion type and intensity estimation directly from brain signals to significantly enhance the accuracy and naturalness of EEG-to-speech/text translation.

The project aims to build a real-time EEG-to-speech system applicable for:

– Individuals with complete speech disability

– Speech rehabilitation for autism, depression, stroke, or partial speech loss

– Brain-driven large language models (LLM)

Research Objectives

– Develop a high-accuracy semantic EEG-to-speech/text translation system that works with limited data.

– Introduce visual feedback loops to enhance error-free selection across large vocabulary dictionaries.

– Estimate emotion type and level from EEG signals and incorporate them into the generated speech.

– Build a real-time model for imagined speech and speech disability rehabilitation.

This project aims to develop the first-generation semantic brain-to-speech system, opening transformative opportunities for healthcare, neuroscience, and AI-driven communication technologies.

Project Contact

For further details, please contact Prof. Saeid Sanei via email: [email protected]

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