10 papers
Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
Lincan Li, Rikuto Kotoge, Xihao Piao +2
Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynam…
Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
Kohei Obata, Taichi Murayama, Zheng Chen +2
Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various ap…
Selective Denoising Diffusion Model for Time Series Anomaly Detection
Kohei Obata, Zheng Chen, Yasuko Matsubara +2
Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity an…
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Haohui Jia, Zheng Chen, Lingwei Zhu +6
Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model con…
RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
Haohui Jia, Zheng Chen, Lingwei Zhu +4
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning…
TIFO: Time-Invariant Frequency Operator for Stationarity-Aware Representation Learning in Time Series
Xihao Piao, Zheng Chen, Lingwei Zhu +3
Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt…