Akram Sahar, Simon Jonathan Z, Babadi Behtash
IEEE Trans Biomed Eng. 2017 Aug;64(8):1896-1905. doi: 10.1109/TBME.2016.2628884. Epub 2016 Nov 15.
A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments.
The TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an ℓ-regularized least squares estimator which is capable of producing dynamic TRF estimates as well as confidence intervals at sampling resolution from single-trial MEG data.
We evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute.
Application of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution.
Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology.
计算神经科学中的一个核心问题是利用从大脑记录的神经活动来表征大脑功能，且这种表征要具有统计置信度，这些神经活动是大脑对感觉输入的响应。现有的大多数估计技术，比如基于反向相关的技术，存在两个主要局限性：第一，它们无法以与记录数据相当的分辨率生成神经活动的动态估计；第二，为了构建用于精确解释数据的统计置信区间，它们通常需要在时间上以及多次试验中进行大量平均。在本文中，我们针对在竞争说话者环境中作为选择性听觉注意的参数计算模型的听觉时间响应函数（TRF）估计，解决上述问题。
TRF是一个稀疏核，它根据语音流的包络对听觉脑磁图（MEG）数据进行回归。我们通过利用TRF的稀疏性并采用一种ℓ正则化最小二乘估计器，开发了一种有效的估计技术，该估计器能够从单次试验的MEG数据中以采样分辨率生成动态TRF估计以及置信区间。
我们在一个有两个竞争说话者的听觉注意实验中，使用来自人脑的诱发MEG反应评估了我们提出的估计器的性能。使用所提出的技术以多秒分辨率随时间动态估计TRF，这比之前时间分辨率在分钟量级的结果有显著改进。
将我们的方法应用于MEG数据，揭示了在多秒分辨率下，M50和M100诱发反应相对于受试者注意状态的调制的精确特征。
我们提出的估计技术在多说话者环境中提供了一个高分辨率实时注意力解码框架，在智能助听器技术中有潜在应用。
