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arXiv 2020年

作者： Luo, Yun Zhu, Li-Zhen Wan, Zi-Yu Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China

The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Our proposed methods are based on two deep generative models, variational autoencoder (VAE) and generative adversarial network (GAN), and two data augmentation strategies. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data, while for partial usage, only high-quality data are selected and appended to the training dataset. These three methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN). To evaluate the effectiveness of these methods, we perform a systematic experimental study on two public EEG datasets for emotion recognition, namely, SEED and DEAP. We first generate realistic-like EEG training data in two forms: power spectral density and differential entropy. Then, we augment the original training datasets with a different number of generated realistic-like EEG data. Finally, we train support vector machines and deep neural networks with shortcut layers to build affective models using the original and augmented training datasets. The experimental results demonstrate that the augmented training datasets produced by our methods enhance the performance of EEG-based emotion recognition models and outperform the existing data augmentation methods such as conditional VAE, Gaussian noise, and rotational data augmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Electrophysiology

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LogisticLDA: Regularizing latent dirichlet allocation by logistic regression

PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference ...

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PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation 2009年 1卷 160-169页

作者： Guo, Jia-Cheng Lu, Bao-Liang Li, Zhiwei Zhang, Lei Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200040 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200040 China Microsoft Research Asia 49 Zhichun Road Haidian District Beijing 100080 China

ISBN: (纸本)9789624423198

We present in this paper a supervised topic model for multi-class classification problems. To incorporate supervisory information, we jointly model documents and their labels in a graphical model called LogisticLDA, which mathematically integrates a generative model and a discriminative model in a principled way. By maximizing the posterior of document labels using logistic normal distributions, the model effectively incorporates the supervisory information to maximize inter-class distance in the topic space, while documents still enjoy the interchangeability characteristic for ease of inference. Experimental results on three benchmark datasets demonstrate that the model outperforms state-of-the-art supervised topic models. Compared with support vector machine, the model also achieves comparable performance, but meanwhile it discovers a topic space, which is valuable for dimension reduction, topic mining and document retrieval. © 2009 by Jia-Cheng Guo, Bao-Liang Lu, Zhiwei Li, and Lei Zhang.

关键词： Normal distribution

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Extracting keyphrases from Chinese news articles using Text Rank and query log knowledge

PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference ...

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PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation 2009年 2卷 733-740页

作者： Liang, Weiming Huang, Chang-Ning Li, Mu Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China Microsoft Research Asia 49 Zhichun Road Haidian District Beijing 100080 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)9789624423198

Keyphrases extracted from articles are beneficial in helping people boost browsing speed, but unfortunately keyphrases are rarely available for news articles due to the high expense of labor and time for manual annotation. This paper proposes a practical approach to extracting keyphrases for Chinese news articles using the Text Rank and query log knowledge. Previous work is word based, while our approach uses phrase as its basic element. We generate phrases by employing several statistical criteria with the huge amount of queries as a training corpus. We use Text Rank, a graph-based learning algorithm, for extracting keyphrases from Chinese news articles. In addition, two instructive features, lengths and positions of phrases, are incorporated into the Text Rank model. Experimental results demonstrate that our methods improve the performance significantly. © 2009 by Weiming Liang, Chang-Ning Huang, Mu Li, and Bao-Liang Lu.

关键词： Graphic methods

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Multimodal Emotion Recognition Using Deep Generalized Canonical Correlation Analysis with an Attention Mechanism

Multimodal Emotion Recognition Using Deep Generalized Canoni...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yu-Ting Lan Wei Liu Bao-Liang Lu Center for Brain-like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Since multimodal learning is able to take advantage of the complementarity of multimodal signals, the performance of multimodal emotion recognition usually surpasses that based on a single modality. In this paper, we introduce deep generalized canonical correlation analysis with an attention mechanism (DGCCA-AM) to multimodal emotion recognition. This model extends the conventional canonical correlation analysis (CCA) from two modalities to arbitrarily numerous modalities and implements multimodal adaptive fusion with an attention mechanism. By adjusting the weights matrices to maximize the generalized correlation of different modalities, DGCCA-AM extracts emotion-related information from multiple modalities and discards noises. The attention mechanism allows a neural network to learn adaptive fusion weights for different modalities and produces a more effective multimodal fusion and superior emotion recognition performance. We evaluate DGCCA-AM on a public multimodal dataset, SEED-V. Our experimental results demonstrate that DGCCA-AM achieves a state-of-the-art mean accuracy of 82.11% and standard deviation of 2.76% for five emotion classifications with three modalities.

关键词： Feature extraction Emotion recognition Correlation Electroencephalography brain modeling Standards Covariance matrices

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

arXiv

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： brain computer interface

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EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks

EEG-Eye Movements Cross-Modal Decision Confidence Measuremen...

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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Cheng Fei Rui Li Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People's Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People's Republic of China

Decision confidence is an individual's feeling of correctness or optimization when making a decision. Various physiological signals, including electroencephalography (EEG) and eye movements have been studied extensively in measuring levels of decision confidence in humans. While multimodal fusion generally performs better than single-modal approaches, it requires data from different modalities at a greater cost. In particular, collection of EEG data is more complicated and time consuming while eye movement signals are much easier to acquire. To tackle this problem, we propose a cross-modal method based on generative adversarial learning. In our method, the intrinsic relationship between eye movement and EEG features in a high-level feature space can be learned in the training phase, and then we can obtain multimodal information during the test phase when only eye movements are available as inputs. Experimental results on the SEED-VPDC dataset demonstrate that our proposed method outperforms single-modal methods trained and tested only on eye movement signals with an improvement of approximately 5.43% in accuracy, and maintains competitive performance in comparison with multimodal methods. Our cross-modal approach requires only eye movements as inputs and reduces reliance on EEG data, making the decision confidence measurement more applicable and practicable.

关键词： Training Costs Neural engineering Particle measurements Generative adversarial networks Electroencephalography Adversarial machine learning

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rong-Fei Gu Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

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Structural stability of lexical semantic spaces: Nouns in Chinese and French

arXiv

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arXiv 2017年

作者： Ploux, Sabine Wang, Rui Zhong, Zheng Feng Zhao, Hai Xin, Yang Lu, Bao-Liang Institut des Sciences Cognitives Marc Jeannerod CNRS/University of Lyon i Bron France Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China East China Normal University Shanghai China Université Libre de Bruxelles Bruxelles Belgium

Many studies in the neurosciences have dealt with the semantic processing of words or categories, but few have looked into the semantic organization of the lexicon thought as a system. The present study was designed to try to move towards this goal, using both electrophysiological and corpus-based data, and to compare two languages from different families: French and Mandarin Chinese. We conducted an EEG-based semantic-decision experiment using 240 words from eight categories (clothing, parts of a house, tools, vehicles, fruits/vegetables, animals, body parts, and people) as the material. A data-analysis method (correspondence analysis) commonly used in computational linguistics was applied to the electrophysiological signals. The present cross-language comparison indicated stability for the following aspects of the languages' lexical semantic organizations: (1) the living/nonliving distinction, which showed up as a main factor for both languages;(2) greater dispersion of the living categories as compared to the nonliving ones;(3) prototypicality of the animals category within the living categories, and with respect to the living/nonliving distinction;and (4) the existence of a person-centered reference gradient. Our electrophysiological analysis indicated stability of the networks at play in each of these processes. Stability was also observed in the data taken from word usage in the languages (synonyms and associated words obtained from textual corpora). Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings

Objective Depression Detection Using EEG and Eye Movement Si...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Luyu Liu Dan Peng Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings—20 positive and 20 negative—as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively; b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments; c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs; d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

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Detecting Major Depression Disorder with Multiview Eye Movement Features in a Novel Oil Painting Paradigm

Detecting Major Depression Disorder with Multiview Eye Movem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tian-Fang Ma Lu-Yu Liu Li-Ming Zhao Dan Peng Yong Lu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Emotion Helper Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Major Depressive Disorder (MDD) is a debilitating condition marked by persistent low mood, reduced interest, cognitive impairments, and vegetative neurological symptoms such as sleep and appetite disturbances. In this paper, we collected eye movement signals from 40 patients diagnosed with MDD and 40 healthy controls to study the relation between eye movements and cognitive processes for depression detection. The eye movement data were captured during a novel emotional cognition task using oil paintings. Subsequently, the data were transformed into multiview eye movement features, including heatmaps, trajectories, and statistical vectors. Rigorous statistical analyses were then conducted on these features to identify significant patterns and correlations between eye movements and depressive symptoms. A multiview invariant & specific eye movement model (MISEYE) was proposed to fuse different eye movement features. The proposed achieved an accuracy rate of 79.88% in depression detection. This performance surpassed not only the outcomes of single-mode approaches and combinations of any two features but also outperformed other fusion methodologies. These findings not only shed light on the intricate relationship between eye movement patterns and MDD but also underscore the potential of eye-tracking technology in psychiatric research.

关键词： Heating systems Accuracy Statistical analysis Oils Depression Feature extraction Vectors

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