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The fNIRS-based Emotion Recognition by Spatial Transformer and WGAN Data Augmentation Towards Developing A Novel Affective BCI

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IEEE Transactions on Affective Computing 2024年第2期16卷 875-890页

作者： Si, Xiaopeng Huang, He Yu, Jiayue Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China

The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions involve a large-scale distributed brain network. Compared to electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) offers a higher spatial resolution. It holds greater potential in capturing emotional spatial information, which may foster the development of new affective brain-computer interfaces (aBCI). We proposed a novel self-attention-based deep-learning transformer language model for fNIRS cross-subject emotion recognition, which could automatically learn the emotion's spatial attention weight information with strong interpretability. Besides, we performed data augmentation by introducing the wasserstein generative adversarial networks (WGAN). Results showed: (1) We achieved 84% three-category cross-subject emotion decoding accuracy. The spatial transformer module and WGAN improved the accuracy by 12.8% and 4.3%, respectively. (2) Compared with cutting-edge fNIRS research, we led by 10% in three-category decoding accuracy. (3) Compared with cutting-edge EEG research, we lead by 28% in arousal decoding accuracy, 10% in valence decoding accuracy, and 2% in three-category decoding accuracy. (4) Besides, our approach holds the potential to uncover the brain's spatial encoding mechanism of human emotion processing, providing a new direction for building interpretable artificial intelligence models. © 2010-2012 IEEE.

关键词： Near infrared spectroscopy

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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Study on the Temporal Patterns of CA1 Pyramidal Neuron Activity in Mice Hippocampus under Simulated Spaceflight Composite Environment 46

Study on the Temporal Patterns of CA1 Pyramidal Neuron Activ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Sun, Shufan Ren, Jing Zhao, Meiling Wang, Ling Zheng, Chenguang Yang, Jiajia Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin 300072 China

ISBN: (纸本)9798350371499

With the gradual realization of manned spaceflight goals, the physical and mental health of astronauts has become a core concern. Numerous studies in recent years have indicated that the aerospace special environment (ASE) can lead to alterations in astronaut cognitive function. However, the adaptive changes in neuronal activity underlying these alterations remain unclear. This study utilized animal models and two-photon imaging technology to investigate the adaptive changes in the activity characteristics and synchronicity of pyramidal neurons in the CA1 region of the mice hippocampus under simulated spaceflight composite environment (SSCE). The results indicate that, with prolonged modeling time, the number of active pyramidal neurons in the CA1 region gradually decreases. Significant reductions in both the frequency and amplitude of neuronal calcium transients occur, stabilizing at lower levels. The calcium transient decay constant extends, and significant decorrelation in neuronal calcium activity is observed. Thus, we posit that these changes may constitute crucial factors leading to the alterations in individual cognitive function induced by the aerospace special environment. © 2024 IEEE.

关键词： Mammals

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Low intensity focused ultrasound stimulation targeted on M1 ameliorates neuroinflammation in hemi-parkinsonian rats 46

Low intensity focused ultrasound stimulation targeted on M1 ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Chunchan Sun, Tianyi Liang, Rong Yang, Zihui Yang, Weifan Li, Wanzhe Yin, Yiwei Zhang, Guanglu Wang, Ling Yang, Jiajia He, Feng Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin 300072 China

ISBN: (纸本)9798350371499

Objective: Low intensity focused stimulation (LIFUS) has been proved to improve motor function in Parkinson's disease (PD) animal modules. The aim of this study is to investigate whether LIFUS target on the primary motor cortex (M1) can improve motor deficit in the PD rats. Methods: The PD rat model was induced by injection of 6-hydroxydopamine (6-OHDA) in the medial forebrain bundle (MFB). Two weeks after the injection, LIFUS was used on PD rats for two weeks. Behavioral tests were performed including open field test and rotarod test to examine the motor ability of the rats. The activity of microglia and astrocyte were tested to evaluate the inflammation level in the brain. The tyrosine hydroxylase (TH) staining was done to detect the recovery of dopaminergic (DA) neurons in the substantia nigra (SN) and DA fibers in the striatum (STR). Results: LIFUS treatment decreased the resting time in OFT(p © 2024 IEEE.

关键词： Neurodegenerative diseases

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Transcriptomic Study of neural Regulation in the Medial Prefrontal Cortex of Depressive Mice with Low-Intensity Focused Ultrasound 46

Transcriptomic Study of Neural Regulation in the Medial Pref...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Cai, Qiuquan Meng, Lianghui Wang, Ling Ren, Jing Yang, Jiajia Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin University Tianjin 300072 China Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin 300072 China

ISBN: (纸本)9798350371499

Exploring new effective treatments for depression holds important social significance and clinical value. Low-intensity focused ultrasound stimulation (LIFUS) has been proven to have significant neuroprotective effects on depression. However, the specific genes regulated by LIFUS in depressive mice remain unclear. We established a depression mice model using chronic restraint stress (CRS) and applied LIFUS to the medial prefrontal cortex (mPFC) following CRS. We explored the impact of modeling and treatment on gene expression changes through transcriptomics, revealing that the therapeutic effect of LIFUS may be associated with the improvement of the activity of calcium signaling pathway and synaptic plasticity. The study provides preclinical evidence and a theoretical basis for applying LIFUS for depression treatment. © 2024 IEEE.

关键词： Mammals

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Maths Anxiety and cognitive state monitoring for neuroadaptive learning systems using electroencephalography 2

Maths Anxiety and cognitive state monitoring for neuroadapti...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and neural engineering, MetroXRAINE 2023

作者： Armani, Federica Daly, Ian Vernitski, Alexei Gillmeister, Helge Scherer, Reinhold School of Computer Science and Electronic Engineering University of Essex Brain-Computer Interfaces and Neural Engineering Laboratory ColchesterCO43SQ United Kingdom University of Essex Department of Mathematical Sciences ColchesterCO43SQ United Kingdom University of Essex Centre for Brain Science Department of Psychology ColchesterCO43SQ United Kingdom

ISBN: (纸本)9798350300802

Mathematical competence is important to acquire for everyday and professional purposes, but often represents a considerable hurdle for students, who may associate it with unpleasant experiences. Our goal is to use neuroscience and neural engineering to support students to improve their mathematical understanding. More specifically, we are interested in the development of a non-invasive electroencephalogram (EEG)-based neuroadaptive brain-computer Interface (BCI) learning environment that optimizes learning outcomes by adapting the learning content provided according to the cognitive load of the learner. In this paper, we investigate what cognitive states occur when students with and without Math Anxiety learn to solve a math problem presented in the form of a novel computer puzzle. Results of an offline analysis of data recorded from 10 study participants suggest that different cognitive states occur, each with specific features that a BCI could potentially detect. © 2023 IEEE.

关键词： brain computer interfaces (BCI) electroencephalogram (EEG) Math Anxiety neuroadaptive technologies

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SEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

SEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xueze Wang, Minjie Chen, Zihua Wang, Lu Wang, Hongbo Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai & Robotics Ministry of Education Shanghai Engineering Research Center of Ai & Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai & Robotics Institude of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798350386226

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios. © 2024 IEEE.

关键词： Deep learning

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Improving Motor Imagery Classification in BCI Systems Using EMD and Multi-Layer CNNs 14

Improving Motor Imagery Classification in BCI Systems Using ...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Arghand, Reza Chaibakhsh, Ali Radman, Moein University of Guilan Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering Rasht Guilan41996-13776 Iran University of Essex Brain-Computer Interfacing and Neural Engineering Laboratory School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798331511272

brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. The features extracted from these networks are combined and used to train an SVM classifier. The proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches. ©2024 IEEE.

关键词： brain computer interface

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Preference detection of the humanoid robot face based on EEG and eye movement

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neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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