检索结果-内蒙古大学图书馆

Editorial: affective brain-computer interface in emotion artificial intelligence and medical engineering

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2023年 17卷 1237252页

作者： Lu, Zhaohua Wang, Tingwen Zhang, Ruirui Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan Peoples R China

[...]the system needs to collect blink data of the previous round of subjects in advance, because different subjects have different blink ranges. [...]subsequent work may need to combine more robust algorithms to classify the blinking of different subjects to improve the performance of the system. [...]oxygen-based brain imaging technology, such as functional magnetic resonance imaging (fMRI), has better spatial resolution and has been widely used in neuroimaging research, which is an important tool for researchers to explore the mechanisms of brain activity (Coupeau et al., 2022). To solve this problem, Fei et al. proposed a symmetric end-to-end trainable hybrid convolutional neural network (HC-Net) for the small-sample characteristics of MRI data and the properties of thick-layer scanning. [...]the experimental results of applying the model to task-based patients with schizophrenia suggest that it can be used as a stable biological reference standard for stability and noise immunity.

关键词： affective brain-computer interface emotion artificial intelligence medical engineering neuroscience brain network

来源：评论

学校读者我要写书评

暂无评论

A bidirectional cross-modal transformer representation learning model for EEG-fNIRS multimodal affective BCI

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Si, Xiaopeng Zhang, Shuai Yang, Zhuobin Yu, Jiayue Ming, Dong Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Tianjin Univ State Key Lab Adv Med Mat & Devices Tianjin 300072 Peoples R China Tianjin Univ Haihe Lab Brain Comp Interact & Human Machine Inte Tianjin 300392 Peoples R China Tianjin Univ Tianjin Key Lab Brain Sci & Neural Engn Tianjin 300072 Peoples R China Tianjin Univ Inst Appl Psychol Tianjin 300072 Peoples R China

By recognizing or regulating human emotions, the affective brain-computer interfaces (BCIs) could improve human-computer interactions. However, human emotion involves complex temporal-spatial brain networks. Therefore, unimodal brain imaging methods have difficulty to decode complex human emotions. Multimodal brain imaging methods, which capture temporal-spatial multi-dimensional brain signals, have been successfully employed in non-affective BCIs, showing extreme potential to improve the affective BCIs. In order to explore a multimodal fusion model with interpretability and improve emotion recognition performance for multimodal affective BCIs. In this study, we propose a Temporal-Spatial Multimodal Fusion (TSMMF) model, which leverages the bidirectional Cross-Modal Transformer (BCMT) to fuse electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal brain signals. Firstly, intra-modal feature extractors and the Self-Attention Transformer were employed to construct joint EEG-fNIRS multimodal representations, reducing inter-modal differences. Secondly, the BCMT was adopted to achieve temporal-spatial multimodal fusion, followed by attention fusion to adaptively adjust the weights of the temporal-spatial multimodal features. Thirdly, modality-specific branches were introduced to preserve the unique features of each modality, then the outputs of all branches were weighted sum for emotion recognition. Furthermore, the model learned the weights of emotion-related brain regions for different modalities. Results showed that: (1) We proposed the first affective BCI based on multimodal brain imaging methods and the emotion recognition outperformed the state-of-the-art methods. (2) An accuracy of 76.15% was achieved for cross-subject emotion decoding, representing improvements of 6.06% and 12.44% compared to EEG and fNIRS unimodal approaches, respectively. (3) The spatial interpretability indicated that: compared to modality-specific branches fo

关键词： affective brain-computer interface Electroencephalography Functional near-infrared spectroscopy Multimodal fusion Bidirectional cross-modal transformer Model interpretability

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Time-Aware Sampling Network With Deep Reinforcement Learning for EEG-Based Emotion Recognition

引用

IEEE TRANSACTIONS ON affective COMPUTING 2024年第3期15卷 1090-1103页

作者： Zhang, Yongtao Pan, Yue Zhang, Yulin Zhang, Min Li, Linling Zhang, Li Huang, Gan Su, Lei Liu, Honghai Liang, Zhen Zhang, Zhiguo Shenzhen Univ Sch Biomed Engn Med Sch Guangdong Prov Key Lab Biomed Measurements & Ultra Shenzhen 518060 Peoples R China Harbin Inst Technol Inst Comp & Intelligence Shenzhen 518000 Peoples R China Marshall Lab Biomed Engn Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518000 Peoples R China

Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible in practice, existing methods can only assign a fixed label to all EEG timepoints in a continuous emotion-evoking trial, which overlooks the highly dynamic emotional states and highly non-stationary EEG signals. To solve the problems of high reliance on fixed labels and ignorance of time-changing information, in this paper we propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion fragments and disregard irrelevant and misleading parts. Specifically, we formulate the process of mining key emotion fragments from EEG time series as a Markov decision process and train a time-aware agent through DRL without label information. First, the time-aware agent takes deep features from a feature extractor as input and generates sample-wise importance scores reflecting the emotion-related information each sample contains. Then, based on the obtained sample-wise importance scores, our method preserves top-X continuous EEG fragments with relevant emotion and discards the rest. Finally, we treat these continuous fragments as key emotion fragments and feed them into a hypergraph decoding model for unsupervised clustering. Extensive experiments are conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion recognition using leave-one-subject-out cross-validation, and the results demonstrate the superiority of the proposed method against previous unsupervised emotion recognition methods. The proposed TAS-Net has great potential in achieving a more practical and accurate affective brain-computer interface in a dynamic and label-free circumstance.

关键词： Electroencephalography Feature extraction Emotion recognition Deep learning brain modeling Convolutional neural networks Reinforcement learning affective brain-computer interface emotion recognition deep reinforcement learning unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

An Efficacy Analysis of Affect Representation Methodologies Used in EEG-Based Emotion Recognition System: An Unsupervised Approach

引用

INTERNATIONAL JOURNAL OF HUMAN-computer INTERACTION 2024年第20期40卷 6401-6417页

作者： Kar, Priyam Hazarika, Jupitara Natl Inst Technol Dept Elect & Instrumentat Engn Silchar India

brain-computer interface (BCI)-based affect recognition in humans utilizing physiological signals is typically guided by a supervised method that relies heavily on the presence of appropriate affect representations. In this paper, we assess the effectiveness of contemporary affect representation methodologies for establishing ground truth for reliable affective BCI systems. On the DEAP dataset, two experiments were conducted to compare the observed emotion clusters based on subjective ratings with the clustering of corresponding physiological responses based on EEG characteristics. The outcomes of the two investigations are inconsistent with one another. The findings suggest the subjective use of emotion categories as opposed to their general application in establishing ground truth for supervised approaches and must encourage researchers to examine affect representation techniques critically before considering them as concrete representations of a person's affective state.

关键词： Emotion recognition affect representation EEG affective brain-computer interface unsupervised approach supervised approach

来源：评论

学校读者我要写书评

暂无评论

PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals

引用

IEEE TRANSACTIONS ON affective COMPUTING 2024年第2期15卷 657-670页

作者： Zhou, Rushuang Zhang, Zhiguo Fu, Hong Zhang, Li Li, Linling Huang, Gan Li, Fali Yang, Xin Dong, Yining Zhang, Yuan-Ting Liang, Zhen Shenzhen Univ Sch Biomed Engn Med Sch Guangdong Prov Key Lab Biomed Measurements & Ultra Shenzhen 518060 Peoples R China City Univ Hong Kong Hong Kong Ctr Cerebro Cardiovasc Hlth Engn COCHE Dept Biomed Engn Kowloon Tong Hong Kong Sci & Technol Pk Hong Kong Peoples R China Harbin Inst Technol Inst Comp & Intelligence Shenzhen 150001 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Educ Univ Hong Kong Dept Math & Informat Technol Ting Kok Hong Kong Peoples R China Univ Elect Sci & Technol China Ctr Informat Med Sch Life Sci & Technol Clin HospChengdu Brain Sci InstMOE Key Lab Neuro Chengdu 610056 Peoples R China City Univ Hong Kong Hong Kong Ctr Cerebrocardiovasc Hlth Engn COCHE Sch Data Sci Kowloon Tong Hong Kong Sci & Technol Pk Hong Kong Peoples R China City Univ Hong Kong Hong Kong Ctr Cerebrocardiovasc Hlth Engn COCHE Dept Biomed Engn Kowloon Tong Hong Kong Sci & Technol Pk Hong Kong 999077 Peoples R China

affective brain-computer interface based on electroencephalography (EEG) is an important branch in the field of affective computing. However, the individual differences in EEG emotional data and the noisy labeling problem in the subjective feedback seriously limit the effectiveness and generalizability of existing models. To tackle these two critical issues, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning (PR-PL). The discriminative and generalized EEG features are learned for emotion revealing across individuals and the emotion recognition task is formulated as pairwise learning for improving the model tolerance to the noisy labels. More specifically, a prototypical learning is developed to encode the inherent emotion-related semantic structure of EEG data and align the individuals' EEG features to a shared common feature space under consideration of the feature separability of both source and target domains. Based on the aligned feature representations, pairwise learning with an adaptive pseudo labeling method is introduced to encode the proximity relationships among samples and alleviate the label noises effect on modeling. Extensive results on two benchmark databases (SEED and SEED-IV) under four different cross-validation evaluation protocols validate the model reliability and stability across subjects and sessions. Compared to the literature, the average enhancement of emotion recognition across four different evaluation protocols is 2.04% (SEED) and 2.58% (SEED-IV).

关键词： brain modeling Electroencephalography Emotion recognition Transfer learning Adaptation models Labeling Computational modeling EEG emotion recognition affective brain-computer interface transfer learning affective computing

来源：评论

学校读者我要写书评

暂无评论

Wired Emotions: Ethical Issues of affective brain-computer interfaces

引用

SCIENCE AND ENGINEERING ETHICS 2020年第1期26卷 351-367页

作者： Steinert, Steffen Friedrich, Orsolya Delft Univ Technol Fac Technol Policy & Management Dept Values Technol & Innovat Delft Netherlands Ludwig Maximilians Univ Munchen Inst Eth Hist & Theory Med Lessingstr 2 D-80336 Munich Germany

Ethical issues concerning brain-computer interfaces (BCIs) have already received a considerable amount of attention. However, one particular form of BCI has not received the attention that it deserves: affective BCIs that allow for the detection and stimulation of affective states. This paper brings the ethical issues of affective BCIs in sharper focus. The paper briefly reviews recent applications of affective BCIs and considers ethical issues that arise from these applications. Ethical issues that affective BCIs share with other neurotechnologies are presented and ethical concerns that are specific to affective BCIs are identified and discussed.

关键词： affective brain-computer interface Emotion brain-computer interface affective states

来源：评论

学校读者我要写书评

暂无评论

F-CACNN: A framework integrating FNet and adaptive attention for end-to-end EEG emotion recognition 9

F-CACNN: A framework integrating FNet and adaptive attention...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Cao, Zhoulei Wang, Xiaoliang Liu, Yuzhen Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350319804

An important area of study in human-computer interaction is emotional brain-computer interface based on electroencephalogram (EEG). A challenge in this area is how to make full use of channel correlation and provide timely feedback on effective emotion recognition results. In this study, we present a FNet-based and channel attention convolution neural network-based model (F-CACNN) for emotion recognition. We thoroughly explore the EEG channel information with the adaptive channel attention mechanism and employ the FNet mechanism for the first time to realize emotion recognition more efficiently. On the one hand, we include the adaptive channel attention technique into the convolutional neural network to adaptively assign the weights according to the importance of each channel and extract EEG spatial features. On the other hand, the FNet mechanism is integrated into long short-term memory. It takes the Fourier Transform to extract the EEG temporal feature without learnable parameterization. Experimental results show that our method can effectively realize emotion recognition without handcrafted features extraction, and significantly reduce time consumption, which meets the current demand for end-to-end emotion recognition. © 2023 IEEE.

关键词： affective brain-computer interface Channel attention mechanism EEG Emotion recognition Fourier Transform

来源：评论

学校读者我要写书评

暂无评论

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

引用

NEURAL COMPUTING & APPLICATIONS 2021年第15期33卷 9061-9073页

作者： Wang, Fei Zhang, Weiwei Xu, Zongfeng Ping, Jingyu Chu, Hao Northeastern Univ Fac Robot Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In real-world application of affective brain-computer interface (aBCI), individual differences across subjects and non-stationary characteristics of electroencephalogram (EEG) signals can cause data bias. Moreover, for new specific subject, the size of sample data is very small compared to that of existing subjects, which easily leads to overfitting in deep neural network training and reduces generalization performance of the network. In this paper, the deep multi-source adaptation transfer network (DMATN) is proposed for the new subjects in aBCI. In DMATN, the multi-source selection is employed to obtain the portion of existing EEG data mostly correlated with new subject and to decrease by two-fifth source data. To explore domain-invariant structures, deep adaptation network is used to map correlated source domain and the target domain (new subject) into reproducing kernel Hilbert space (RKHS) optimized by the multiple kernel variant of maximum mean discrepancies (MK-MMD). To more precisely predict the emotional state of the new subject, domain discriminator is applied in DMATN to make the data distribution of the two domains more similar. Finally, across-subject experiments on SEED dataset are conducted to evaluate the proposed method. The experimental results show that DMATN model can achieve the state-of-the-art performance of 84.46%, 83.32% and 84.90% in three sessions, respectively. It also shows great time efficiency in applications of aBCI.

关键词： affective brain-computer interface Emotion recognition Transfer learning Electroencephalogram (EEG)

来源：评论

学校读者我要写书评

暂无评论

Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism

引用

JOURNAL OF NEURAL ENGINEERING 2023年第6期20卷 066010-066010页

作者： Peng, Dan Zheng, Wei-Long Liu, Luyu Jiang, Wei-Bang Li, Ziyi Lu, Yong Lu, Bao-Liang Shanghai Jiao Tong Univ RuiJin Hosp Clin Neurosci Ctr RuiJin Mihoyo LabSch Med 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China Shanghai Jiao Tong Univ Ctr Brain Like Comp & Machine Intelligence Brain Sci & Technol Res Ctr Dept Comp Sci & Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ RuiJin Hosp Sch Med 197 Ruijin 2nd Rd Shanghai 200020 Peoples R China

Objective. Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the electroencephalography (EEG) neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models. Approach. We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition. Main results. (1) Sex differences exist in various emotion types and both Western and Eastern cultures;(2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males;(3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) the same-sex emotion recognition models outperform the corresponding cross-sex models. Significance. These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.

关键词： affective brain-computer interface emotion recognition EEG sex differences deep learning

来源：评论

学校读者我要写书评

暂无评论

Transformer-based ensemble deep learning model for EEG-based emotion recognition

引用

brain Science Advances 2023年第3期9卷 210-223页

作者： Xiaopeng Si Dong Huang Yulin Sun Shudi Huang He Huang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjin 300072China

Emotion recognition is one of the most important research directions in the field of brain–computer interface(BCI).However,to conduct electroencephalogram(EEG)-based emotion recognition,there exist difficulties regarding EEG signal processing;moreover,the performance of classification models in this regard is *** counter these issues,the 2022 World Robot Contest successfully held an affective BCI competition,thus promoting the innovation of EEG-based emotion *** this paper,we propose the Transformer-based ensemble(TBEM)deep learning *** comprises two models:a pure convolutional neural network(CNN)model and a cascaded CNN-Transformer hybrid *** proposed model won the abovementioned affective BCI competition’s final championship in the 2022 World Robot Contest,demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.

关键词： affective brain-computer interface electroen-cephalogram Transformer deep learning ensemble learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：