基于动觉反馈的神经康复脑机接口

Adithya K, Saurabh Jacob Kuruvila, Sarang Pramode, Niranjana Krupa
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引用次数: 3

摘要

本研究旨在记录和翻译运动意象的过程,以实现手指的矫形伸展和屈曲。本研究从数据采集、深度学习和末端执行器三个方面探讨了开发脑机接口系统的可行性。利用递归特征消去法从64个通道的在线数据集中推导出记录MI数据的最佳通道。使用OpenBCI Cyton试剂盒的8个电极通道,覆盖5名受试者的感觉运动皮层区域,按照标准化的EEG采集方案记录脑电图数据。在使用卷积层和LSTM的自定义深度学习架构上进行任务分类。结果传递给矫形支架,矫形支架提供动觉反馈机制,以提高握力并支持其使用者的神经康复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Computer Interface for Neurorehabilitation with Kinesthetic Feedback
This study aims to record and translate the process of motor imagery to enable orthotic extension and flexion of the finger. This study probes the feasibility of developing a Brain Computer Interface system by prioritizing - Data Acquisition, Deep Learning, and End Effector of the system. The optimal channels to record MI data were deduced using Recursive Feature Elimination from a sixty four channel online dataset. Eight electrode channels of the OpenBCI Cyton kit were used, covering the sensorimotor cortex region of five subjects to record electroencephalographic data by following a standardized EEG acquisition protocol. Classification of tasks was carried out on a custom deep learning architecture using a convolutional layer and LSTM. The results were passed to an orthotic brace that provided a kinesthetic feedback mechanism to improve grip strength and support the neurorehabilitation of its user.
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