Search Results for author: Cuntai Guan

Found 21 papers, 10 papers with code

Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models

no code implementations29 Sep 2021 Davide Coppola, Hwee Kuan Lee, Cuntai Guan

Experiments on the CIFAR10 dataset showed that using only $10\%$ of the full training set, the proposed method was able to adequately defend the model against the AutoPGD attack while maintaining a classification accuracy on clean images outperforming the model with adversarial training by $7\%$.

Adversarial Robustness Image Classification

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

1 code implementation9 Jul 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

Second, we design an iterative self-training strategy to align the fine-grained class distributions for the source and target domains via target domain pseudo labels.

Automatic Sleep Stage Classification Domain Adaptation +1

Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion

1 code implementation2 Jul 2021 Su Zhang, Yi Ding, Ziquan Wei, Cuntai Guan

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 aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information.

Emotion Recognition

Time-Series Representation Learning via Temporal and Contextual Contrasting

1 code implementation26 Jun 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, XiaoLi Li, Cuntai Guan

In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.

Automatic Sleep Stage Classification Contrastive Learning +8

LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

no code implementations5 May 2021 Yi Ding, Neethu Robinson, Qiuhao Zeng, Cuntai Guan

In this paper, we propose LGG, a neurologically inspired graph neural network, to learn local-global-graph representations from Electroencephalography (EEG) for a Brain-Computer Interface (BCI).

EEG EEG Emotion Recognition

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

1 code implementation28 Apr 2021 Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.

Automatic Sleep Stage Classification EEG +1

TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition

1 code implementation7 Apr 2021 Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai Guan

TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.

EEG Emotion Recognition

FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface

1 code implementation17 Mar 2021 Ravikiran Mane, Effie Chew, Karen Chua, Kai Keng Ang, Neethu Robinson, A. P. Vinod, Seong-Whan Lee, Cuntai Guan

With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients.

Classification EEG +1

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

no code implementations22 Jan 2021 Ziyuan Zhao, Zeng Zeng, Kaixin Xu, Cen Chen, Cuntai Guan

We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.

Active Learning Ensemble Learning +1

Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping

1 code implementation10 Oct 2020 Hong Jing Khok, Victor Teck Chang Koh, Cuntai Guan

Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics.

 Ranked #1 on EEG on HS-SSVEP

EEG Multi-Task Learning

Quantifying Explainability of Saliency Methods in Deep Neural Networks

no code implementations7 Sep 2020 Erico Tjoa, Cuntai Guan

One way to achieve eXplainable artificial intelligence (XAI) is through the use of post-hoc analysis methods.

Explainable artificial intelligence

Federated Transfer Learning for EEG Signal Classification

1 code implementation26 Apr 2020 Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan

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 lack of large datasets.

Classification Domain Adaptation +4

TSception: A Deep Learning Framework for Emotion Detection Using EEG

1 code implementation2 Apr 2020 Yi Ding, Neethu Robinson, Qiuhao Zeng, Duo Chen, Aung Aung Phyo Wai, Tih-Shih Lee, Cuntai Guan

TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.

EEG General Classification +1

Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls

no code implementations14 Mar 2020 Mengjiao Hu, Kang Sim, Juan Helen Zhou, Xudong Jiang, Cuntai Guan

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 with schizophrenia from healthy controls.

General Classification

Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)

no code implementations4 Feb 2020 Aung Aung Phyo Wai, Yangsong Zhang, Heng Guo, Ying Chi, Lei Zhang, Xian-Sheng Hua, Seong Whan Lee, Cuntai Guan

We observed that CSTA achieves the maximum mean accuracy of 97. 43$\pm$2. 26 % and 85. 71$\pm$13. 41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis.

Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

no code implementations19 Nov 2019 Erico Tjoa, Guo Heng, Lu Yuhao, Cuntai Guan

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 images provided by ISLES 2017 competition.

Lesion Segmentation

Machine learning driven synthesis of few-layered WTe2

no code implementations10 Oct 2019 Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu

Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

no code implementations17 Jul 2019 Erico Tjoa, Cuntai Guan

Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood.

Explainable artificial intelligence

Machine learning-guided synthesis of advanced inorganic materials

1 code implementation10 May 2019 Bijun Tang, Yuhao Lu, Jiadong Zhou, Han Wang, Prafful Golani, Manzhang Xu, Quan Xu, Cuntai Guan, Zheng Liu

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.

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