Brain big data empowered by intelligent analysis provide an unrivalled opportunity to probe the dynamics of the brain in disorder. A typical example is to identify evolving synchronization patterns from multivariate electroencephalography (EEG) routinely superimposed with intensive noise in epilepsy research and practice. Under the circumstance of insufficient a priori knowledge of subject dependency on domain problem, it becomes even more important to adaptively classify the synchronization dynamics to accurately characterize the intrinsic nature of seizure activities represented by the EEG. This paper first measures the global maximal information coefficient (MIC) of all EEG data channels to form a time sequence of correlation matrices. A lightweight VGGNet (Visual Geometry Group) is designed to adapt to the need to prune massive EEG datasets. The $VGGNet$ characterizes the synchronization dynamics captured in the correlation matrices and then automatically identifies the seizure states of the EEG. Experiments are performed over the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset to evaluate the proposed approach. Seizure states can be identified with an accuracy, sensitivity, and specificity of [98.13%±0.24%], [98.85%±0.51%], and [97.47%±0.36%], respectively; the resulting performance is superior to those of most existing methods over the same dataset. The approach directly applies to raw EEG analysis, which holds great potential for handling brain big data.

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Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC

Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.

/pdf/fuzzy-recurrence-plots-2h63u3bz4h.pdf

Fuzzy recurrence plots

Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. This paper proposes a global feature extraction method that encapsulates the multichannel EEG signals into gray images. The maximal information coefficient (MIC) for all channels was first measured. Subsequently, an MIC matrix was constructed according to the electrode arrangement rules and represented by an MIC gray image. Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. Experiments were conducted on the benchmark dataset for emotion analysis using EEG, physiological, and video signals. The experimental results demonstrated that the global synchronization features and spatial characteristics are beneficial for recognizing emotions and the proposed deep learning model effectively mines and utilizes the two salient features.

/pdf/improved-deep-feature-learning-by-synchronization-7ap6s7xo11.pdf

Guanghua Gu

Papers

A new EEG synchronization strength analysis method

Symbol Recurrence Plots based resting-state eyes-closed EEG deterministic analysis on amnestic mild cognitive impairment in type 2 diabetes mellitus

Image-text bidirectional learning network based cross-modal retrieval

Gradient-based refined class activation map for weakly supervised object localization

Emotion Recognition on EEG Signal Using ResNeXt Attention 2D-3D Convolution Neural Networks