在人类体皮层中,想象运动的神经子空间在几年内保持稳定
L Bashford1, I Rosenthal1, S Kellis1
1Division of Biology and Biological Engineering, and T&C Chen Brain-Machine Interface Center, California Institute of Technology, Pasadena, CA, USA.
bioRxiv : the preprint server for biology
|July 18, 2023
概括
通过使用潜伏子空间对齐,在几年内在人类中实现了稳定的神经解码. 这种方法提高了脑机界面的寿命和通用性,而不需要经常重新训练.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 在脑机界面 (BMI) 中实现神经解码的长期稳定对于实际应用至关重要.
- 当前的BMI通常需要经常重新训练以保持性能,从而限制了它们的可用性.
结论:
- 这种方法显著延长了神经解码器件的寿命和通用性.
- 这些发现为更强大,更易于使用的BMI铺平了道路.
- 通过先进的信号处理技术可以实现稳定的神经解码.
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