Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

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 (MEA) and electrocorticography (ECoG). However, the use of stereoelectroencephalography (sEEG) for speech decoding has not been fully recognized.
Methods: In this research, recently-released sEEG data were used to decode Dutch words spoken by participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, a sequence-to-sequence model (seq2seq), and a transformer model.
Results: Our RNN-based seq2seq 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 few of the electrodes.
Conclusion: This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.