Voice Reconstruction from Brain Signals
The algorithm aimed at reconstructing voice from EEG during imagined speech.
Key Contributions- A generative model capable of extracting frequency characteristics and sequential information from neural signals to generate speech.
- Addressed the constraint of imagined speech-based BTS system lacking ground truth voice by employing a domain adaptation method.
- Demonstrated the potential of robust speech generation by training only several words or phrases, with the model showing the capability to learn phoneme level information from brain signals.
- This work is currently accepted for presentation at AAAI 2023.
- This work is based on Neurotalk. We will continue to develop and extend this foundational work..
EEG Imagined Speech Decoding Using Diffusion-based Learning
Decoding EEG signals for imagined speech has been a complex task, primarily due to the high-dimensional nature of the data and a low signal-to-noise ratio.
Key Contributions- Our study introduces Diff-E, a novel method that utilizes denoising diffusion probabilistic models (DDPMs) and a conditional autoencoder to address these challenges.
- We've found that Diff-E substantially outperforms traditional machine learning techniques and baseline models in terms of decoding accuracy.
- These findings indicate the potential effectiveness of DDPMs for EEG signal decoding, suggesting possible applications in the development of brain-computer interfaces that enable communication through imagined speech.
- This work is currently accepted for presentation at Interspeech 2023.
- This work is based on Diff-E. We will continue to develop and extend this foundational work.
This folder contains code for the topic 'Reconstructing Sentences from Brain Signals using Contextual and Semantic Information'. It will be updated in the near future.
This OnlineDemo folder will continue to be updated for an online demo system that is currently under development.
- A comprehensive collection of Brain-Computer Interfaces (BCIs) and EEG signal analysis codes
- Focusing on EEG data analysis, BCIs development, and motor imagination analysis respectively.
- The folders offer varied functionalities like Motor Imagery, Steady-State Visual Evoked Potential, Event-Related Potential analysis, GUI module, and Paradigm functions.
- For further information or inquiries, visit (http://openbmi.org).
