A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials
A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials
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DOI：
10.1109/tnnls.2015.2496284
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发表时间：
2017-04
影响因子：
10.4
通讯作者：
Chaohua Wu;Ke Lin;Wei Wu;Xiaorong Gao
中科院分区：
文献类型：
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作者：
Chaohua Wu;Ke Lin;Wei Wu;Xiaorong Gao
近年来，脑机接口（BCI）被认为是一种很有前途的融合人类智能和机器智能的技术。目前，基于事件相关电位（ERP）的脑机接口是基于无创脑电图（EEG）的脑机接口的一个重要分支。从有限数量的试验中提取ERP仍然具有挑战性，因为它们的低信噪比（SNR）和体积传导引起的低空间分辨率。在本文中，我们提出了一个概率模型的尝试由试验级联脑电，其中级联的ERP表示为一组离散的正弦和余弦基地的线性组合。基数仅由单个试验的数据长度确定。一个稀疏的空间-光谱模式矩阵的秩的先验引入到模型中，以允许自动确定的组件的数量。基于循环下降的最大后验估计算法，然后开发的空间光谱模式估计。然后可以通过最大化ERP分量的SNR来获得空间滤波器。通过对13名被试的合成数据和真实的N170事件相关电位数据的实验，验证了算法的有效性。实验结果表明，与现有的几种算法相比，本文提出的算法能够更准确地估计ERP。
Recent years have witnessed brain–computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases. The bases are simply determined by the data length of a single trial. A sparse prior on the rank of the spatio-spectral pattern matrix is introduced into the model to allow the number of components to be automatically determined. A maximum posterior estimation algorithm based on cyclic descent is then developed to estimate the spatiospectral patterns. A spatial filter can then be obtained by maximizing the SNR of the ERP components. Experiments on both synthetic data and real N170 ERP from 13 subjects were conducted to test the efficacy and efficiency of the algorithm. The results showed that the proposed algorithm can estimate the ERPs more accurately than the several state-of-the-art algorithms.