Decoding movement information from cortical activity for invasive BMIs

Min-Ki Kim, Sung-Phil Kim
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引用次数: 1

Abstract

Most invasive brain-machine interfaces (BMIs) have relied on the movement-related information in the firing activities of a number of cortical neurons. Recently, many efforts have been made to represent high-dimensional firing activities of a neuronal ensemble in a low-dimensional features space, visualizing the trajectory of temporal evolution of neural activities. The resulting neural trajectory often provides a sound means to visualize encoding of movement information in neuronal ensembles as well as to improve decoding performance by eliminating noise from irrelevant neurons. The present study aims to build the neural trajectory from motor cortical neurons in a primate performing a center-out task. The neural trajectory built by the standard principal component analysis method well represented hand speed profiles and provided proper feature vectors to a subsequent decoding algorithm. The results suggest an effective way of single-trial speed decoding for invasive BMIs.
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从侵入性bmi的皮质活动中解码运动信息
大多数侵入性脑机接口(BMIs)依赖于许多皮质神经元放电活动中的运动相关信息。近年来,研究人员在低维特征空间中对神经元集合的高维发射活动进行了表征,以可视化神经活动的时间演化轨迹。由此产生的神经轨迹通常提供了一种声音手段来可视化神经元集合中运动信息的编码,以及通过消除无关神经元的噪声来提高解码性能。本研究旨在建立灵长类动物运动皮层神经元在执行“中心向外”任务时的神经轨迹。采用标准主成分分析法构建的神经轨迹能够很好地表征手速度特征,为后续的解码算法提供了合适的特征向量。结果提示了一种有效的侵入性bmi单次快速解码方法。
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