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EEG-based Emotion Recognition in Immersive Virtual Reality: Meeting the Requirement of Accuracy and Computational Efficiency 23

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EEG-based Emotion Recognition in Immersive Virtual Reality: ...

7th International Conference on Digital Signal Processing, ICDSP 2023

作者： Pei, Guanxiong Fan, Cunhang Li, Taihao Jin, Jia Wang, Rui Research Institute of Artificial Intelligence Zhejiang Lab China School of Computer Science and Technology Anhui University China Laboratory of Applied Brain and Cognitive Sciences Shanghai International Studies University China School of Business and Management Shanghai International Studies University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9781450398626

Virtual reality technology provides a strong sense of immersion and interactivity. It is widely used in the fields of anxiety relief, fear therapy, and depression regulation. However, objectively evaluating the emotional intervention effect of virtual reality technology is a difficult problem. The main purpose of this paper is to explore the use of EEG signals to identify individual emotional states in virtual reality scenarios and to improve the computational efficiency and recognition accuracy of emotional valence. To induce the target emotional state of the participants, we established a relatively standard emotion-induced virtual reality video library. The EEG data of the participants were collected synchronously as they watched the virtual reality video. The results show that the emotion recognition performance of multiple features (energy spectrum, differential entropy, differential asymmetry, and rational asymmetry) is better than that of a single feature. The radial basis function neural network (RBFNN) performed better than the deep belief network (DBN). RBFNN achieves the highest average classification accuracy of 91.1%. By combining the feature selection (F-test) method with the RBFNN, an ideal classification performance can be maintained with computational efficiency improvements. Furthermore, it is demonstrated that the features extracted from the theta band outperform features extracted from other bands in emotional valence decoding. These results may contribute to the application of EEG-based affective computing technology in the field of psychological rehabilitation and assessment. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

Gaze depth estimation for eye-tracking systems

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Gaze depth estimation for eye-tracking systems

Multimodal Image Exploitation and Learning 2023

作者： Stone, Abigail Rajeev, Srijith Rao, Shishir Paramathma Panetta, Karen Agaian, Sos Gardony, Aaron Nordlund, Jessica Skantar, Rebecca Department of Electrical and Computer Engineering Tufts University MedfordMA02155 United States Department of Computer Science City University of New York New YorkNY10016 United States U.S. Army Combat Capabilities Development Command Soldier Center NatickMA01760 United States Center for Applied Brain and Cognitive Sciences Tufts University MedfordMA02155 United States

ISBN: (数字)9781510661677

ISBN: (纸本)9781510661660

Eye-tracking holds numerous promises for improving the mixed reality experience. While eye-tracking devices are capable of accurate gaze mapping on 2D surfaces, depth estimation of gaze points remains a challenging problem. Most gaze-based interaction applications are supported by estimation techniques that find a mapping between gaze data and corresponding targets on a 2D surface. This approach inevitably leads to a biased outcome, as the nearest objects in the line of sight will tend to be the target of interest more often. One viable solution would be to estimate gaze as a 3D coordinate (x, y, z) rather than the traditional 2D coordinate (x, y). This article first introduces a new comprehensive 3D gaze dataset collected in a realistic scene setting. Data was collected using a head-mounted eye-tracker and a depth estimation camera. Next, we present a novel depth estimation model, trained on the new gaze dataset to accurately predict gaze depth based on calibrated gaze vectors. This method could help develop a mapping between gaze and 3D objects on a 3D plane. The presented model improves the reliability of depth measurement of visual attention in real scenes as well as the accuracy of depth-based scenes in virtual reality environments. Improving situational awareness using 3D gaze data will benefit several domains, particularly human-vehicle interaction, autonomous driving, and augmented reality. © 2023 SPIE.

关键词： Mapping

Spectral-temporal-spatial customization via modulating multimodal nonlinear pulse propagation

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arXiv 2023年

作者： Qiu, Tong Cao, Honghao Liu, Kunzan Yu, Li-Yu Levy, Manuel Lendaro, Eva Wang, Fan You, Sixian Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States

Multimode fibers (MMFs) have recently reemerged as attractive avenues for nonlinear effects due to their high-dimensional spatiotemporal nonlinear dynamics and scalability for high power. High-brightness MMF sources with effective control of the nonlinear processes would offer new possibilities for a wide range of applications from high-power fiber lasers, to bioimaging and chemical sensing, and to novel physics phenomena. Here we present a simple yet effective way of controlling nonlinear effects at high peak power levels: by leveraging not only the spatial but also the temporal degrees of freedom of the multimodal nonlinear pulse propagation in step-index MMFs using a programmable fiber shaper. This method represents the first method that enables modulation and optimization of multimodal nonlinear pulse propagation, achieving high tunability and broadband high peak power. Its potential as a nonlinear imaging source is further demonstrated by applying the MMF source to multiphoton microscopy, where widely tunable two-photon and three-photon imaging is achieved with adaptive optimization. These demonstrations highlight the effectiveness of directly modulating multimodal nonlinear pulse propagation to enhance the high-dimensional customization and optimize the high spectral brightness of MMF output. These advancements provide new possibilities for technology advances in nonlinear optics, bioimaging, spectroscopy, optical computing, and material processing. © 2023, CC BY.

关键词： Multimode fibers

The Impact of Transient and Stable Patterns of Functional Connectivity in Emotion Recognition

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The Impact of Transient and Stable Patterns of Functional Co...

IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Yinghao Huang Xucheng Liu Ye Li Chio-In Ieong Yong Hu Feng Wan Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China Shenzhen Institute of Advanced Technology Chinese Academy Sciences Shenzhen China Guangdong Institute of Intelligence Science and Technology Zhuhai China Department of Orthopaedics and Traumatology The University of Hong Kong Hong Kong China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The achievement of emotional functions relies on the interactions of various functional systems of the human brain. Numerous studies tried to explore the mechanism of the emotions based on the functional connectivity. However, the contribution of the transient and stable patterns of brain communication in brain emotions was still unclear. The recently proposed activation network framework assumed the activity of functional connectivity (AFC) and background of functional connectivity (BFC) to respectively represent transient and stable patterns of functional connectivity. In this paper, we employed the activation network framework to SEED-IV dataset to achieve the emotion recognition and evaluate the performance of the transient and stable patterns in emotional activities. The top 100 critical connections of each subject were extracted by a data-driven feature selection strategy. The critical connections across all subjects of both AFC and BFC suggested the importance of Gamma band in emotion recognition. Especially, the AFC and BFC showed the different communication modes during the emotions. Finally, the subject-independent classification was employed on each subject’s critical connections to achieve the emotion recognition. The BFC showed the best classification accuracy of 78.71% ± 1.73% (mean ± std). The findings demonstrated that human emotions were mostly influenced by the consistent brain communication patterns. The findings of this investigation offer a new insight on the studies of human emotion.

关键词： Emotion recognition Accuracy Virtual environments Feature extraction Electroencephalography Transient analysis Computational intelligence

Elevating Robust Multi-Talker ASR by Decoupling Speaker Separation and Speech Recognition

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arXiv 2025年

作者： Yang, Yufeng Taherian, Hassan Kalkhorani, Vahid Ahmadi Wang, DeLiang Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Center for Cognitive and Brain Sciences The Ohio State University 1835 Neil Ave ColumbusOH43210 United States

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train the backend on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the speaker separation frontend and the ASR backend, with the latter trained on clean speech only. Our decoupled system achieves 5.1% word error rates (WER) on the Libri2Mix dev/test sets, significantly outperforming other multi-talker ASR baselines. Its effectiveness is also demonstrated with the state-of-the-art 7.60%/5.74% WERs on 1-ch and 6-ch SMS-WSJ. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 2.92%. These state-of-the-art results suggest that decoupling speaker separation and recognition is an effective approach to elevate robust multi-talker ASR. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images

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A Distributed Inference System for Detecting Task-wise Singl...

International Winter Workshop on brain-computer Interface (BCI)

作者： Sung-Jin Kim Heon-Gyu Kwak Hyeon-Taek Han Dae-Hyeok Lee Ji-Hoon Jeong Dept. of Artificial Intelligence Korea University Seoul Republic of Korea Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Dept. of Computer Science Chungbuk National University Cheongju Republic of Korea

brain-computer interface (BCI) has garnered the significant attention for their potential in various applications, with event-related potential (ERP) performing a considerable role in BCI systems. This paper introduces a novel Distributed Inference System tailored for detecting task-wise single-trial ERPs in a stream of satellite images. Unlike traditional methodologies that employ a single model for target detection, our system utilizes multiple models, each optimized for specific tasks, ensuring enhanced performance across varying image transition times and target onset times. Our experiments, conducted on four participants, employed two paradigms: the Normal paradigm and an AI paradigm with bounding boxes. Results indicate that our proposed system outperforms the conventional methods in both paradigms, achieving the highest $F_{\beta}$ scores. Furthermore, including bounding boxes in the AI paradigm significantly improved target recognition. This study underscores the potential of our Distributed Inference System in advancing the field of ERP detection in satellite image streams.

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brain2depth: Lightweight CNN model for classification of cognitive states from EEG recordings

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arXiv 2021年

作者： Pandey, Pankaj Miyapuram, Krishna Prasad Computer Science and Engineering Centre for Cognitive and Brain Sciences IIT Gandhinagar

Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback. © 2021, CC BY.

关键词： Electroencephalography

DOMAIN RANDOMIZATION VIA ENTROPY MAXIMIZATION

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arXiv 2023年

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D’Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent’s behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography