Search Results for author: Bao-liang Lu

Found 26 papers, 3 papers with code

Focused State Recognition Using EEG with Eye Movement-Assisted Annotation

no code implementations15 Jun 2024 Tian-Hua Li, Tian-Fang Ma, Dan Peng, Wei-Long Zheng, Bao-liang Lu

Utilizing deep learning models for learning EEG and eye movement features proves effective in classifying brain activities.

EEG

Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals

no code implementations27 Jul 2023 Yu-Ting Lan, Kan Ren, Yansen Wang, Wei-Long Zheng, Dongsheng Li, Bao-liang Lu, Lili Qiu

Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery.

EEG Image Reconstruction +1

PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition

no code implementations29 Sep 2021 Yun Luo, Gengchen Wei, Bao-liang Lu

Usually, the DA methods give relatively promising results than the DG methods but require additional computation resources each time a new subject comes.

Domain Generalization EEG +2

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs

no code implementations NeurIPS 2020 Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

1 code implementation26 Oct 2020 Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-liang Lu, Hao Su

Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.

Document-level Neural Machine Translation with Document Embeddings

no code implementations16 Sep 2020 Shu Jiang, Hai Zhao, Zuchao Li, Bao-liang Lu

Standard neural machine translation (NMT) is on the assumption of document-level context independent.

Machine Translation NMT +1

Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models

no code implementations4 Jun 2020 Yun Luo, Li-Zhen Zhu, Zi-Yu Wan, Bao-liang Lu

Then, we augment the original training datasets with a different number of generated realistic-like EEG data.

Data Augmentation EEG +2

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

no code implementations13 Apr 2020 Dongrui Wu, Yifan Xu, Bao-liang Lu

Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly.

EEG Motor Imagery +1

Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

no code implementations4 Apr 2020 Xun Wu, Wei-Long Zheng, Bao-liang Lu

The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP.

Brain Computer Interface Clustering +2

Document-level Neural Machine Translation with Associated Memory Network

no code implementations31 Oct 2019 Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-liang Lu

Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.

Machine Translation NMT +2

Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis

1 code implementation13 Aug 2019 Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-liang Lu

We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets.

Binary Classification General Classification +1

Judging Chemical Reaction Practicality From Positive Sample only Learning

no code implementations22 Apr 2019 Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-liang Lu, Ning Xia

Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference.

Semi-supervised Bayesian Deep Multi-modal Emotion Recognition

no code implementations25 Apr 2017 Changde Du, Changying Du, Jinpeng Li, Wei-Long Zheng, Bao-liang Lu, Huiguang He

In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data.

Emotion Recognition Imputation

Connecting Phrase based Statistical Machine Translation Adaptation

no code implementations COLING 2016 Rui Wang, Hai Zhao, Bao-liang Lu, Masao Utiyama, Eiichro Sumita

Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains with the original corpus can indeed enhance SMT performance directly.

Machine Translation Sentence +1

Multimodal Emotion Recognition Using Multimodal Deep Learning

no code implementations26 Feb 2016 Wei Liu, Wei-Long Zheng, Bao-liang Lu

To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models from multiple physiological signals.

EEG Multimodal Deep Learning +1

Identifying Stable Patterns over Time for Emotion Recognition from EEG

no code implementations10 Jan 2016 Wei-Long Zheng, Jia-Yi Zhu, Bao-liang Lu

In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach.

BIG-bench Machine Learning EEG +2

On the Reducibility of Submodular Functions

no code implementations4 Jan 2016 Jincheng Mei, Hao Zhang, Bao-liang Lu

The scalability of submodular optimization methods is critical for their usability in practice.

Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks

no code implementations IEEE Transactions on Autonomous Mental Development ( Volume: 7 , Issue: 3 , Sept. 2015 ) 2015 Wei-Long Zheng, Bao-liang Lu

To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative.

EEG Emotion Recognition

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