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

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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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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Heat transfer simulation of optogenetics-LED based on microchannel heat dissipation

Heat transfer simulation of optogenetics-LED based on microc...

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2024 International Conference on Optoelectronic Information and computer engineering, OICE 2024

作者： Liu, Lusheng Le, Song Zhang, Yuan Zhang, Jing Wang, Aiping Bin, Jianxiong Niu, Lan Zhang, Lihua 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 and Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9781510682504

Optogenetics technology has greatly promoted the development of neuroscience. Flexible gene manipulation tools and sensitive light activation methods have provided convenience for neural function research. However, the thermal effect generated in the process of light stimulation affects the activation of neurons by optogenetic technology, which makes the results of light-activated light-sensitive proteins to generate neural currents inaccurate. Here, we established an in vivo activation model of LED-based optogenetic technology through simulation and analyzed the thermal effect produced by it. We propose to use microchannels to dissipate heat from implanted devices, reducing the thermal effect of light stimulation for neural activation. Through simulation, we propose the use of the vortex-like and cobweb-like microchannels for heat dissipation, which can control the temperature rise within 0.28K and 0.31K, respectively. © 2024 SPIE.

关键词： Heat losses

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

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Adaptation models Calcium Neurons neural networks Mental health Information processing Mice Decorrelation Transient analysis Hippocampus

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

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Ultrasonic imaging Calcium Transcriptomics Depression brain modeling Mice Regulation Gene expression Synapses Stress

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

Low intensity focused ultrasound stimulation targeted on M1 ...

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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<0.05) and increased the latency to falls in the rotarod test(p<0.05) compared with the untreated PD rats. Moreover, LIFUS reduced the inflammation response reflected in microglia and astrocyte activation. Additionally, TH-immunoreactive fibers increased in the STR after LIFUS. Conclusion: These findings demonstrated that LIPUS targeted on M1 can inhibit neuroinflammation and improve movement disorders of PD rats. Significance: This study provides a new therapeutic strategy for further clinical application in PD.

关键词： Primary motor cortex Ultrasonic imaging Neurons Rats Forebrain Motors brain modeling Optical fiber testing engineering in medicine and biology Diseases

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A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

A discussion of statistical criteria for assessing awareness...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Amaunam, Idorenyin Schneider, Christoph Da Silva, Marina Lopes Jöhr, Jane Diserens, Karin Perdikis, Serafeim Brain-Computer Interface and Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Department of Radiology BostonMA United States Acute Neurorehabilitation Unit Division of Neurology Centre Hospitalier Universitaire Vaudois Department of Clinical Neurosciences Lausanne Switzerland

ISBN: (纸本)9798350337020

This work discusses the implications of selecting particular statistical metrics and thresholds as criteria to diagnose awareness through brain-computer Interface (BCI) technology in patients with Disorders of Consciousness (DOC). We report a first analysis of a novel dataset collected to investigate whether a motor attempt electroencephalography (EEG) paradigm coupled with Functional Electrical Stimulation (FES) can detect command following and, therefore, signs of conscious awareness in DOC. We assessed 22 DOC patients admitted to the acute rehabilitation unit after a brain lesion over one or more sessions. We extracted EEG sensorimotor rhythms and performed a standard open-loop BCI pipeline evaluation, classifying motor attempt against resting-state trials. We validate this approach by correlating classification accuracy with the established clinical scale Coma Recovery Scale Revised. We employ a machine learning (ML)-inspired diagnostic criterion based on confidence intervals over chance-level classification accuracy and show that it yields more conservative and, arguably, reliable inference of Cognitive Motor Dissociation (CMD) by means of command-following, neuroimaging-based tools, compared to diagnoses based on clinical assessments or criteria examining the statistical significance of brain features across different mental states. © 2023 IEEE.

关键词： awareness brain-computer interface chance-level disorders of consciousness motor attempt

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Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition

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computers in Biology and Medicine 2024年 181卷 108973-108973页

作者： Si, Xiaopeng Huang, Dong Liang, Zhen Sun, Yulin Huang, He Liu, Qile Yang, Zhuobin Ming, Dong 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 Tianjin University Tianjin300072 China School of Biomedical Engineering Medical School Shenzhen University Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China

Emotion recognition is crucial for human–computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://***/ThreePoundUniverse/MACTN. © 2024 Elsevier Ltd

关键词： Emotion Recognition

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The feasibility of combining communication BCIs with FES for individuals with locked-in syndrome

arXiv

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arXiv 2023年

作者： Canny, Evan Vansteensel, Mariska J. van der Salm, Sandra M.A. Müller-Putz, Gernot R. Berezutskaya, Julia Brain Center Department of Neurology and Neurosurgery University Medical Center Utrecht Utrecht Netherlands Institute of Neural Engineering Laboratory of Brain-Computer Interfaces Graz University of Technology Graz Austria

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in the brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals. © 2023, CC BY.

关键词： brain computer interface

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Inter-Participant Transfer Learning with Attention Based Domain Adversarial Training for P300 Detection

SSRN

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SSRN 2024年

作者： Li, Shurui Daly, Ian Guan, Cuntai Cichocki, Andrzej Jin, Jing Center of Intelligent Computing School of Mathematics East China University of Science and Technology Shanghai200237 China Brain-Computer Interfacing Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Nanyang Technological University 639798 Singapore RIKEN Brain Science Institute Wako351–0198 Japan Torun87-100 Poland Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

A brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which require training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source Domain transfer based Attention Domain Adversarial neural Network (OADANN) to mitigate data distribution discrepancies for participant-independent ERP classification tasks. We train and validate our proposed model using both a publicly available OpenBMI dataset and a self-collected dataset, employing a leave-one-participant-out cross-validation scheme. Our experimental results demonstrate that our OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowCovNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method. © 2024, The Authors. All rights reserved.

关键词： brain computer interface

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