Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech
Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech
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DOI：
10.1109/tbme.2022.3185005
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Joshua P. Kulasingham;J. Simon
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文献类型：
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作者：
Joshua P. Kulasingham;J. Simon
目的时间反应函数（TRF）是一个线性的神经活动模型，它被时间锁定在连续刺激（包括连续语音）上。基于语音包络的TRF通常具有不同的组件，这些组件为语音的皮层处理提供了显着的见解。然而，目前的方法可能会导致不太可靠的估计单一主题的TRF组件。在这里，我们比较了两个既定的方法，在TRF分量估计，并提出了新的算法，利用这些组件的先验知识，绕过完整的TRF估计。方法我们比较了两个已建立的算法，岭和boosting，和两个新的算法的基础上，子空间追踪（SP）和期望最大化（EM），直接估计TRF组件给出合理的假设有关的组件特性。单通道，多通道，和源定位TRF的模拟和真实的脑磁图数据进行了拟合。性能指标包括模型拟合和组件估计精度。结果Boosting和岭估计在分量估计中具有相当的性能。新算法在模拟中优于其他算法，但在真实的数据上却不是，这可能是由于合理的假设实际上没有得到满足。与boosting相比，Ridge对真实的数据的模型拟合稍好，但也有更多的虚假TRF活动。结论平滑（脊）和稀疏（提升）算法在TRF分量估计上都有较好的性能。SP和EM算法可能是准确的，但依赖于组件特性的假设。意义这一系统的比较建立了广泛使用的和新的算法估计强大的TRF组件，这是必不可少的改进的特定主题的调查到皮层处理的语音的适用性。
Objective The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation. Methods We compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized TRFs were fit on simulations and real magnetoencephalographic data. Performance metrics included model fit and component estimation accuracy. Results Boosting and ridge have comparable performance in component estimation. The novel algorithms outperformed the others in simulations, but not on real data, possibly due to the plausible assumptions not actually being met. Ridge had slightly better model fits on real data compared to boosting, but also more spurious TRF activity. Conclusion Results indicate that both smooth (ridge) and sparse (boosting) algorithms perform comparably at TRF component estimation. The SP and EM algorithms may be accurate, but rely on assumptions of component characteristics. Significance This systematic comparison establishes the suitability of widely used and novel algorithms for estimating robust TRF components, which is essential for improved subject-specific investigations into the cortical processing of speech.