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Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy

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  • 1State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Journal of Neural Engineering
|June 5, 2020
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Summary

Integrating posterior parietal cortex (PPC) signals with primary sensorimotor cortex data significantly improves brain-machine interface (BMI) hand gesture decoding accuracy for paralyzed individuals.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Hand movement is vital for daily activities.
  • Brain-machine interfaces (BMIs) offer functional independence for paralyzed individuals.
  • Existing intracranial electroencephalography (iEEG)-based BMIs primarily utilize sensorimotor cortex signals, overlooking the posterior parietal cortex (PPC).

Purpose of the Study:

  • To investigate the potential of combining iEEG signals from the PPC with those from the primary sensorimotor cortex.
  • To enhance hand gesture decoding performance in iEEG-based BMIs.

Main Methods:

  • Stereoelectroencephalography (SEEG) signals were recorded from 25 epilepsy patients during a three-class hand gesture task.
  • High gamma power (55-150 Hz) was analyzed to evaluate electrode activation and gesture selectivity in the PPC, postcentral cortex (POC), and precentral cortex (PRC).
  • Linear support vector machine classifiers were used to decode gestures and compare decoding accuracies.

Main Results:

  • A majority of electrodes across all regions showed significant task-related activation.
  • A temporal activation sequence was observed: PPC activated first, followed by PRC, then POC.
  • Combining electrodes from PPC, PRC, and POC improved gesture decoding accuracy by 3.6% to 8% compared to using only PRC and POC.

Conclusions:

  • The PPC contains neural information crucial for fine hand movement, supporting its role in hand shape encoding.
  • Integrating PPC with the primary sensorimotor cortex enhances iEEG-based BMI performance.
  • The PPC is a valuable neural source for advanced BMI applications, demonstrating early involvement in visuomotor tasks.