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

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  • Additional Information
    • Source:
      Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE
    • Publication Information:
      Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004-
    • Subject Terms:
      Deep Learning* ; Electroencephalography*/methods ; Speech*/physiology ; Brain-Computer Interfaces*; Humans ; Male ; Female ; Epilepsy/physiopathology ; Epilepsy/diagnosis ; Stereotaxic Techniques ; Adult ; Neural Networks, Computer
    • 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.
      (Creative Commons Attribution license.)
    • Contributed Indexing:
      Keywords: brain–computer interface (BCI); deep learning; speech decoding; speech prosthesis; stereo-electroencephalography (sEEG)
    • Publication Date:
      Date Created: 20240617 Date Completed: 20240627 Latest Revision: 20240627
    • Publication Date:
      20240627
    • Accession Number:
      10.1088/1741-2552/ad593a
    • Accession Number:
      38885688