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Decoding personalized motor cortical excitability states from human electroencephalography.

Sara J Hussain1, Romain Quentin2

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Machine learning accurately predicts individual motor cortex excitability using electroencephalography (EEG) signals. This brain state identification enables personalized transcranial magnetic stimulation (TMS) for tailored neuromodulation.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability.
  • Current group-level approaches lack individual specificity.

Purpose of the Study:

  • To test if machine learning classifiers can discriminate high and low motor cortex (M1) excitability states in individuals.
  • To utilize low-density electroencephalography (EEG) signals for this discrimination.

Main Methods:

  • Analysis of a dataset with 600 TMS pulses, EEG, and electromyography (EMG) recordings in 20 healthy adults.
  • Application of multivariate pattern classification to differentiate brain states associated with varying motor-evoked potentials (MEPs).

Main Results:

  • Personalized classifiers achieved 80% accuracy in discriminating M1 excitability states for individual participants.
  • MEPs were significantly larger during predicted high excitability states (90% of participants).
  • Classifiers did not generalize across different participants, highlighting individual specificity.

Conclusions:

  • Individual brain activity patterns predict M1 excitability states and can be captured by low-density EEG.
  • Individualized classifiers are crucial for effective brain state-dependent TMS.
  • This approach paves the way for fully personalized neuromodulation therapies.