Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
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DOI：
10.1088/1741-2552/ab1a1f
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发表时间：
2019-08-01
影响因子：
4
通讯作者：
Tarokh, Vahid
中科院分区：
文献类型：
--
作者：
Angjelichinoski, Marko;Banerjee, Taposh;Tarokh, Vahid
Objective.我们考虑的问题，预测眼动的目标，通过多电极阵列记录在猕猴前额叶皮层的局部场电位（LFP）。猴子的任务是对八个目标之一进行记忆引导的扫视，在此期间记录LFP活动并用于训练解码器。Approach.以往的研究主要依赖于LFP的频谱幅度作为解码特征，而忽略了相位，没有适当的理论依据。本文阐述了问题的解码眼动意图在统计最优的框架，并使用高斯序列建模和Pinsker的定理，以产生最小最大最优估计的LFP信号被用作解码功能。该方法被示出为充当低通滤波器，并且特征空间中的每个LFP在适当收缩之后经由其复傅立叶系数来表示，使得更高频率分量被衰减;这样，固有地存在于LFP信号中的相位信息被自然地嵌入到特征空间中。主要结果。我们表明，所提出的复杂的基于频谱的解码器实现了高达94%的预测精度在表面附近的前额叶皮层皮层的表层皮层深度，这标志着一个显着的性能改善传统的基于功率谱的解码器。意义所提出的分析展示了低通滤波的LFP信号用于预期运动动作的高度可靠的神经解码的有前途的潜力。
Objective. We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. Approach. Previous reports have mainly relied on the spectral amplitude of the LFPs as decoding feature, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker's theorem to generate minimax-optimal estimates of the LFP signals which are used as decoding features. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space. Main results. We show that the proposed complex spectrum-based decoder achieves prediction accuracy of up to 94% at superficial cortical depths near the surface of the prefrontal cortex; this marks a significant performance improvement over conventional power spectrum-based decoders. Significance. The presented analyses showcase the promising potential of low-pass filtered LFP signals for highly reliable neural decoding of intended motor actions.