Tensor analysis of electroencephalogram signal for localization of event-related potentials
Tensor analysis of electroencephalogram signal for localization of event-related potentials
复制标题
用于事件相关电位定位的脑电图信号张量分析
DOI：
--
复制
发表时间：
--
期刊：
影响因子：
--
通讯作者：
S. Pouryazdian
中科院分区：
文献类型：
--
作者：
S. Pouryazdian
脑电图（EEG）广泛用于监测、诊断和研究大脑的生理、精神和功能异常。脑电图是一种高维信号，大脑对信息的处理表现为脑电活动在时间、频率和空间上的动态变化。EEG信号处理倾向于将这些变化描述和量化为具有已知时空谱特性的函数，或者至少更容易表征。多通道脑电图记录自然包括多种模式。矩阵分析是将其他模式与保留的两种模式叠加或连接起来的方法，已被广泛用于脑电图数据的表示和分析。另一方面，多路（张量）分析技术保持了数据的结构，通过同时分析更多的维度，将数据总结成更多可解释的组件。
Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brains physiological, mental and functional abnormalities. EEG is known to be a high-dimensional signal in which processing of information by the brain is reected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations into functions with known spatio-temporal-spectral properties or at least easier to characterize. Multi-channel EEG recordings naturally include multiple modes. Matrix analysis, via stacking or concatenating other modes with the retained two modes, has been extensively used to represent and analyze the EEG data. On the other hand, Multi-way (tensor) analysis techniques keep the structure of the data, and by analyzing more dimensions simultaneously, summarize the data into more interpretable components.
This work presents a generalized multi-way array analysis methodology in pattern classification systems as related to source separation and discriminant feature selection in EEG signal processing problems. Analysis of ERPs, as one of the main categories of EEG signals, requires systems that can exploit the variation of the signals in different contextual domains in order to reveal the hidden structures in the data.
Temporal, spectral, spatial, and subjects/experimental conditions of multi-channel ERP signals are exploited here to generate three-way and four-way ERP tensors. Two key elements of this framework are the Time-Frequency representation (TFR) and CANDECOMP/PARAFAC model order selection techniques we incorporate for analysis. Here, we propose a fully data-driven TFR scheme, via combining the Empirical Mode Decomposition
and Reassignment method, which yields a high resolution and cross-term free TFR. Furthermore, we develop a robust and effective model order selection scheme that outperforms conventional techniques in mid and low SNRs (i.e. 0?10 dB) with a better Probability of Detection (PoD) and almost no extra computational overhead after the CANDECOMP/PARAFAC decomposition.
ERP tensor can be regarded as a mixture that includes different kinds of brain activity,
artifacts, interference, and noise. Using this framework, the desired brain activity could
be extracted out from the mixture. The extracted signatures are then translated for
different applications in brain-computer interface and cognitive neuroscience.