Abstract
Objective: Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.
Approach: In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.
Main results: Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.
Significance: This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.
Original language | English |
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Article number | 036055 |
Number of pages | 14 |
Journal | Journal of Neural Engineering |
Volume | 21 |
Issue number | 3 |
Early online date | 27 Jun 2024 |
DOIs | |
Publication status | Published - 30 Jun 2024 |
Data Availability Statement
The data cannot be made publicly available upon publication because no suitable repository exists for hosting data in this field of study. The data that support the findings of this study are available upon reasonable request from the authors.Funding
This work is supported by EPSRC New Horizons Grant of UK (EP/X018342/1).
Funders | Funder number |
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EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) | EP/X018342/1 |
Keywords
- brain-computer interface (BCI)
- deep learning
- speech decoding
- speech prosthesis
- stereo-electroencephalography (sEEG)
ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience