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Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces.

Ariel Tankus1,2,3, Noam Rosenberg4, Oz Ben-Hamo4

  • 1Functional Neurosurgery Unit, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel.

Journal of Neural Engineering
|April 22, 2024
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Summary

Single neuron activity in the thalamus

Keywords:
brain-machine interfacedecodinghuman neurophysiologysingle unit recordingsspeechventralis intermediate nucleus of the thalamus (Vim)vowels

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • The ventralis intermediate nucleus (Vim) of the thalamus plays a role in motor control and may be involved in speech.
  • Decoding neural activity is crucial for developing effective brain-machine interfaces (BMIs).

Purpose of the Study:

  • To decode single neuron firing patterns in the left Vim related to speech production, perception, and imagery.
  • To determine the number of Vim neurons required for accurate speech decoding in BMIs.

Main Methods:

  • Intraoperative single neuron recordings from the left Vim in eight neurosurgical patients.
  • Utilized the Spade decoder, a machine learning algorithm based on sparse decomposition, to analyze neural firing patterns during speech tasks.
  • Recorded neural activity during speech production, perception, and imagery of vowel sounds.

Main Results:

  • The Spade decoder achieved high accuracies: 100% for production, 96% for perception, and 92% for imagery (chance level: 20%).
  • Decoding accuracy showed a logarithmic relationship with the number of neurons used for all speech aspects.
  • Speech production decoding consistently yielded higher accuracies than perception and imagery.

Conclusions:

  • Single neuron activity in the left Vim is a promising input source for speech-related BMIs.
  • Understanding the neuron count needed for decoding accuracy is vital for planning BMI implantation for patients with speech impairments.