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Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine.
EURASIP Journal on Advances in Signal Processing ( IF 1.9 ) Pub Date : 2008-01-01 , DOI: 10.1155/2008/592742
Zhisong Wang 1 , Alexander Maier , Nikos K Logothetis , Hualou Liang
Affiliation  

We propose an empirical mode decomposition (EMD-) based method to extract features from the multichannel recordings of local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs) with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP) approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI).

中文翻译:

通过集成经验模式分解、聚类和支持向量机对双稳态感知进行单次试验分类。

我们提出了一种基于经验模式分解 (EMD-) 的方法,从从猕猴的中间时间 (MT) 视觉皮层收集的局部场电位 (LFP) 的多通道记录中提取特征,用于解码其双稳态结构 -运动 (SFM) 感知。特征提取方法包括三个阶段。首先,我们使用 EMD 将非平稳单次试验时间序列分解为称为固有模式函数 (IMF) 的窄带分量,其时间尺度取决于数据。其次,我们采用无监督的 K 均值聚类将 IMF 和残差分组到所有试验和渠道中的几个集群中。第三,我们使用有监督的公共空间模式 (CSP) 方法为聚类时空信号设计空间滤波器。我们在提取的特征上利用支持向量机 (SVM) 分类器在单次试验的基础上解码报告的感知。我们证明了伽马频带中集群的 CSP 特征优于其他频带中的特征,并导致最佳解码性能。我们还表明,基于 EMD 的特征提取可用于诱发电位估计。我们提出的特征提取方法可能适用于许多涉及非平稳多变量时间序列的应用,例如脑机接口 (BCI)。我们还表明,基于 EMD 的特征提取可用于诱发电位估计。我们提出的特征提取方法可能适用于许多涉及非平稳多变量时间序列的应用,例如脑机接口 (BCI)。我们还表明,基于 EMD 的特征提取可用于诱发电位估计。我们提出的特征提取方法可能适用于许多涉及非平稳多变量时间序列的应用,例如脑机接口 (BCI)。
更新日期:2019-11-01
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