Search Results for author:
Found 6 papers, 3 papers with code
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning
Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD).
TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging
Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep learning-based sleep staging.
Fine-Grained Attention for Weakly Supervised Object Localization
Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discriminative parts.
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.
Multi-Scale Neural network for EEG Representation Learning in BCI
Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning.
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning.
