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The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography.

Qinwan Rabbani1, Griffin Milsap2, Nathan E Crone3

  • 1Department of Electrical Engineering, The Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA. qrabbani@jhu.edu.

Neurotherapeutics : the Journal of the American Society for Experimental Neurotherapeutics
|January 9, 2019
PubMed
Summary
This summary is machine-generated.

Brain-computer interfaces (BCIs) offer real-time speech communication via neural signals, aiding those with severe speech disorders. This review explores current advancements and future potential for speech BCIs using electrocorticography (ECoG).

Keywords:
Automatic speech recognition (ASR)Brain–computer interface (BCI)CommunicationElectrocorticography (ECoG)Locked-in syndrome (LIS)Neural speech decoding

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science
  • Speech Technology

Background:

  • Brain-computer interfaces (BCIs) leverage neural activity to restore or enhance user capabilities.
  • Speech BCIs aim to enable real-time communication through neural correlates of attempted or imagined speech.
  • This technology holds promise for improving the quality of life for individuals with severe communication impairments, such as locked-in syndrome.

Purpose of the Study:

  • To review the current state-of-the-art in speech brain-computer interfaces (BCIs).
  • To explore the potential of using chronic electrocorticography (ECoG) for speech BCIs.
  • To identify challenges and opportunities in translating speech BCI technology to clinical applications.

Main Methods:

  • Review of recent developments in neural decoders, feature extraction, and brain recording modalities.
  • Analysis of insights from automatic speech recognition (ASR) and related fields.
  • Examination of studies demonstrating neural speech decoding (semantic, auditory, articulatory) using electrocorticography (ECoG).

Main Results:

  • Recent studies show promise in neural speech decoding using ECoG, identifying semantic, auditory, and articulatory information.
  • The mesoscopic scale of ECoG surface electrode arrays may offer advantages for speech BCIs due to the distributed nature of speech representations in the brain.
  • Significant advancements in neural decoding and speech recognition technologies are facilitating progress in speech BCI development.

Conclusions:

  • Speech BCIs have the potential to restore communication for individuals with severe speech disorders.
  • Electrocorticography (ECoG) presents a promising modality for capturing the distributed neural signals necessary for speech decoding.
  • Further research and development are needed to overcome challenges in translating speech BCIs for widespread clinical use.