Brain EEG Time-Series Clustering Using Maximum-Weight Clique

Representative Point-Based Clustering With Neighborhood Information for Complex Data Structures

Discovering clusters remains challenging when dealing with complex data structures, including those with varying densities, arbitrary shapes, weak separability, or the presence of noise. In this article, we propose a novel clustering algorithm called representative point-based clustering with neighborhood information (RPC-NI), which highlights the significance of neighborhood information often overlooked by existing clustering methods. The proposed algorithm first introduces a new local centrality metric that integrates both neighborhood density and topological convergence to identify core representative points, effectively capturing the structural characteristics of the data. Subsequently, a density-adaptive distance is defined to evaluate dissimilarities between these core representative points, and such distance is used to construct a minimum spanning tree (MST) over these points. Finally, an MST-based clustering algorithm is employed to yield the desired clusters. Incorporating neighborhood information enables RPC-NI to comprehensively determine representative points, and having multiple representative points per cluster allows RPC-NI to adapt to clusters of arbitrary shapes, varying densities, and different sizes. Extensive experiments on widely used datasets demonstrate that RPC-NI outperforms baseline algorithms in terms of clustering accuracy and robustness. These results provide further evidence for the importance of incorporating neighborhood information discovering clusters with complex structures.

Dynamic Graph Representation Learning for Spatio-Temporal Neuroimaging Analysis

Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, existing neuroimaging studies focused primarily on spatial functional connectivity, despite temporal dynamics in complex brain networks. To address this gap, we propose a spatio-temporal interactive graph representation framework (STIGR) for dynamic neuroimaging analysis that encompasses different aspects from classification and regression tasks to interpretation tasks. STIGR leverages a dynamic adaptive-neighbor graph convolution network to capture the interrelationships between spatial and temporal dynamics. To address the limited global scope in graph convolutions, a self-attention module based on Transformers is introduced to extract long-term dependencies. Contrastive learning is used to adaptively contrast similarities between adjacent scanning windows, modeling cross-temporal correlations in dynamic graphs. Extensive experiments on six public neuroimaging datasets demonstrate the competitive performance of STIGR across different platforms, achieving state-of-the-art results in classification and regression tasks. The proposed framework enables the detection of remarkable temporal association patterns between regions of interest based on sequential neuroimaging signals, offering medical professionals a versatile and interpretable tool for exploring task-specific neurological patterns. Our codes and models are available at https://github.com/77YQ77/STIGR/.

SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.

OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition

Electroencephalogram(EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently,semi-supervisedlearning exhibits promisingemotion recognition performance by involving unlabeled EEG data into model training. However, if we first build a graph to characterize the sample similarities and then perform label propagation on this graph, these two steps cannotwell collaborate with each other. In this paper, we propose an OptimalGraph coupledSemi-Supervised Learning (OGSSL) model for EEG emotion recognition by unifying the adaptive graph learning and emotion recognition into a single objective. Besides, we improve the label indicator matrix of unlabeledsamples in order to directly obtain theiremotional states. Moreover, the key EEG frequency bands and brain regions in emotion expression are automatically recognized by the projectionmatrix of OGSSL. Experimental results on the SEED-IV data set demonstrate that 1) OGSSL achieves excellent average accuracies of 76.51%, 77.08% and 81.29% in three cross-sessionemotion recognition tasks, 2) OGSSL is competent for discriminative EEG feature selection in emotion recognition, and 3) the Gamma frequency band, the left/righttemporal, prefrontal,and (central) parietal lobes are identified to be more correlated with the occurrence of emotions.