2.4k citations · 3.2k across the 20 of their papers we have counts for
20 papers
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
Yi Ding, Su Zhang, Chuangao Tang +1
Emotion recognition using electroencephalogram (EEG) mainly has two scenarios: classification of the discrete labels and regression of the continuously tagged labels. Although many…
Interpretable and Robust AI in EEG Systems: A Survey
Xinliang Zhou, Chenyu Liu, Jinan Zhou +5
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has substantially advanced human-computer interaction (HCI) technologies in the AI era. Differen…
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
Ce Ju, Reinmar Josef Kobler, Cuntai Guan
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input…
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Ce Ju, Cuntai Guan
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG). Over the past few decades, the perform…
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Ce Ju, Cuntai Guan
Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imag…
Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion
Su Zhang, Yi Ding, Ziquan Wei +1
We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an a…
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep s…
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Shumeng Li, Ziyuan Zhao, Kaixin Xu +2
Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain…
Convolutional Neural Network Interpretability with General Pattern Theory
Erico Tjoa, Guan Cuntai
Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefi…
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul +7
Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded…
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
Ziyuan Zhao, Zeng Zeng, Kaixin Xu +2
Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Thi…
Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
Erico Tjoa, Guan Cuntai
The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intell…
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
Erico Tjoa, Cuntai Guan
Post-hoc analysis is a popular category in eXplainable artificial intelligence (XAI) study. In particular, methods that generate heatmaps have been used to explain the deep neural…
TSception: A Deep Learning Framework for Emotion Detection Using EEG
Yi Ding, Neethu Robinson, Qiuhao Zeng +4
In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional laye…
Federated Transfer Learning for EEG Signal Classification
Ce Ju, Dashan Gao, Ravikiran Mane +3
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lac…
Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls
Mengjiao Hu, Kang Sim, Juan Helen Zhou +2
Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but not yet been applied to differentiating patients wi…
Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks
Erico Tjoa, Guo Heng, Lu Yuhao +1
We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal i…
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
Erico Tjoa, Cuntai Guan
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, esp…
Machine learning-guided synthesis of advanced inorganic materials
Bijun Tang, Yuhao Lu, Jiadong Zhou +6
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complex…
How transcranial direct current stimulation facilitates post-stroke rehabilitation
Feng Qi, Xin Hong, ZhongKang Lu +7
Whether transcranial direct current stimulation (tDCS) benefits stroke rehabilitation remains unclear. To investigate how tDCS reorganizes brain circuitry, nineteen post-stroke pat…