1 citations · 1 across the 1 of their papers we have counts for
4 papers
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
Dynamic GNNs, which integrate temporal and spatial features in Electroencephalography (EEG) data, have shown great potential in automating seizure detection. However, fully capturi…
Intrinsic frequency distribution characterises neural dynamics
Ryohei Fukuma, Yoshinobu Kawahara, Okito Yamashita +3
Decomposing multivariate time series with certain basic dynamics is crucial for understanding, predicting and controlling nonlinear spatiotemporally dynamic systems such as the bra…
Wirelessly transmitted subthalamic nucleus signals predict endogenous pain levels in Parkinson's disease patients
Abdi Reza, Takufumi Yanagisawa, Naoki Tani +5
Parkinson disease (PD) patients experience pain fluctuations that significantly reduce their quality of life. Despite the vast knowledge of the subthalamic nucleus (STN) role in PD…
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
Rikuto Kotoge, Zheng Chen, Tasuku Kimura +4
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics…