Pub Date : 2023-09-01DOI: 10.26599/BSA.2023.9050018
Chengzhi Yi, Yuxuan Wu, Fan Ye, Xinchen Zhang, Jingjing Chen
The steady-state visual evoked potential (SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface (BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest (WRC) 2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB. The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.
基于稳态视觉诱发电位(SSVEP)的拼写方法因其快速处理和在不同个体间的一致表现而成为当前脑机接口(BCI)系统中广泛采用的一种范式。与基于校准的SSVEP算法相反，无需校准的SSVEP算法提供了清晰直观的数学原理，使新手开发人员可以访问它们。在2022年世界机器人大赛(WRC)期间，本科生组的参与者利用各种方法在无校准设置下完成目标检测，并成功地使用MATLAB实现了算法。在最后的测试中，获胜的方法实现了198.94 bit /min的平均信息传输速率，在无校准的情况下，这是非常高的。本文介绍了所选方法的基本原理，并通过分析最终测试和离线实验的结果对其有效性进行了比较。此外，我们建议WRC的青少年比赛可以作为对研究和开发自己的BCI系统感兴趣的初学者的理想起点。
{"title":"Overview of recognition methods for SSVEP-based BCIs in World Robot Contest 2022: MATLAB undergraduate group","authors":"Chengzhi Yi, Yuxuan Wu, Fan Ye, Xinchen Zhang, Jingjing Chen","doi":"10.26599/BSA.2023.9050018","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050018","url":null,"abstract":"The steady-state visual evoked potential (SSVEP)-based speller has emerged as a widely adopted paradigm in current brain–computer interface (BCI) systems due to its rapid processing and consistent performance across different individuals. Calibration-free SSVEP algorithms, as opposed to their calibration-based counterparts, offer clear and intuitive mathematical principles, making them accessible to novice developers. During the World Robot Contest (WRC) 2022, participants in the undergraduate category utilized various approaches to accomplish target detection in the calibration-free setting, successfully implementing the algorithms using MATLAB. The winning approach achieved an average information transfer rate of 198.94 bits/min in the final test, which is notably high given the calibration-free scenario. This paper presents an introduction to the underlying principles of the selected methods, accompanied by a comparison of their effectiveness through analysis of results from both the final test and offline experiments. Additionally, we propose that the youth competition of WRC could serve as an ideal starting point for beginners interested in studying and developing their own BCI systems.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43871430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/BSA.2023.9050013
Zehui Wang, Hongfei Zhang, Zhouyu Ji, Yuliang Yang, Hongtao Wang
The rapid serial visual presentation (RSVP) paradigm has garnered considerable attention in brain–computer interface (BCI) systems. Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models. In this study, we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022. We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection. The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks. We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.
{"title":"A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022","authors":"Zehui Wang, Hongfei Zhang, Zhouyu Ji, Yuliang Yang, Hongtao Wang","doi":"10.26599/BSA.2023.9050013","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050013","url":null,"abstract":"The rapid serial visual presentation (RSVP) paradigm has garnered considerable attention in brain–computer interface (BCI) systems. Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models. In this study, we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022. We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection. The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks. We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42960946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/BSA.2023.9050010
Zhenbang Du, Rui Bian, Dongrui Wu
In recent years, the steady-state visual evoked potential (SSVEP) electroencephalogram paradigm has gained considerable attention owing to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP-based brain–computer interface (BCI) systems. In SSVEP-based BCIs, the asynchronous scenario poses a challenge as the subjects stare at the screen without synchronization signals from the system. The algorithm must distinguish whether the subject is being stimulated or not, which presents a significant challenge for accurate classification. In the 2022 World Robot Contest Championship, several effective algorithm frameworks were proposed by participating teams to address this issue in the SSVEP competition. The efficacy of the approaches employed by five teams in the final round is demonstrated in this study, and an overview of their methods is provided. Based on the final score, this paper presents a comparative analysis of five algorithms that propose distinct asynchronous recognition frameworks via diverse statistical methods to differentiate between intentional control state and non-control state based on dynamic window strategies. These algorithms achieve an impressive information transfer rate of 89.833 and a low false positive rate of 0.073. This study provides an overview of the algorithms employed by different teams to address asynchronous scenarios in SSVEP-based BCIs and identifies potential future avenues for research in this area.
{"title":"Overview of the winning approaches in 2022 World Robot Contest Championship–Asynchronous SSVEP","authors":"Zhenbang Du, Rui Bian, Dongrui Wu","doi":"10.26599/BSA.2023.9050010","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050010","url":null,"abstract":"In recent years, the steady-state visual evoked potential (SSVEP) electroencephalogram paradigm has gained considerable attention owing to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP-based brain–computer interface (BCI) systems. In SSVEP-based BCIs, the asynchronous scenario poses a challenge as the subjects stare at the screen without synchronization signals from the system. The algorithm must distinguish whether the subject is being stimulated or not, which presents a significant challenge for accurate classification. In the 2022 World Robot Contest Championship, several effective algorithm frameworks were proposed by participating teams to address this issue in the SSVEP competition. The efficacy of the approaches employed by five teams in the final round is demonstrated in this study, and an overview of their methods is provided. Based on the final score, this paper presents a comparative analysis of five algorithms that propose distinct asynchronous recognition frameworks via diverse statistical methods to differentiate between intentional control state and non-control state based on dynamic window strategies. These algorithms achieve an impressive information transfer rate of 89.833 and a low false positive rate of 0.073. This study provides an overview of the algorithms employed by different teams to address asynchronous scenarios in SSVEP-based BCIs and identifies potential future avenues for research in this area.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49450719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/BSA.2023.9050011
Jiayu An, Xinru Chen, Dongrui Wu
From August 19 to 21, 2022, the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing, China. Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI. This paper introduces the algorithms in the motor imagery (MI) classification area, describes the competition content and set, and summarizes the algorithms and results of the top five teams in the finals. First, the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced, followed by the introduction of the algorithms of the top five teams in the final step by step, including electroencephalography channel selection, data length selection, data preprocessing, data augmentation, classification network, training, and testing settings. Finally, the highlights and results of each algorithm are discussed.
{"title":"Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey","authors":"Jiayu An, Xinru Chen, Dongrui Wu","doi":"10.26599/BSA.2023.9050011","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050011","url":null,"abstract":"From August 19 to 21, 2022, the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing, China. Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI. This paper introduces the algorithms in the motor imagery (MI) classification area, describes the competition content and set, and summarizes the algorithms and results of the top five teams in the finals. First, the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced, followed by the introduction of the algorithms of the top five teams in the final step by step, including electroencephalography channel selection, data length selection, data preprocessing, data augmentation, classification network, training, and testing settings. Finally, the highlights and results of each algorithm are discussed.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46695210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.26599/BSA.2023.9050012
Hangjie Yi, Dongjun Liu, Xuanyu Jin, Han-mei Zhang, Wanzeng Kong
The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.
{"title":"Winning algorithms in BCI Controlled Robot Contest in World Robot Contest 2022: BCI Turing Test","authors":"Hangjie Yi, Dongjun Liu, Xuanyu Jin, Han-mei Zhang, Wanzeng Kong","doi":"10.26599/BSA.2023.9050012","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050012","url":null,"abstract":"The Turing Test is a method of testing whether a machine has human intelligence. A novel brain–computer interface (BCI) Turing Test was proposed in the BCI Controlled Robot Contest in World Robot Contest 2022. Contestants developed algorithms that can distinguish if an instruction is issued by a human. Participants collaborated with an electroencephalogram-based BCI to play a soccer game in a virtual scenario. Participants were asked to perform steady-state visual evoked potential (SSVEP) tasks or motor imagery (MI) tasks to control the robots or be in an idle state to mimic the system giving instructions on behalf of the participants. Several algorithms proposed in this competition are developed based on the concept that the idle state is a category in multiclass classification problems. This paper details the algorithms of the top five teams with the best performance in the final, lists the popular classification models and algorithms for MI and SSVEP, and discusses the effectiveness of each approach in improving classification performance and reducing the computation time.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46756984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.26599/BSA.2023.9050015
Zhongyan Shi, Lei Ding, Xingyu Han, Bo Jiang, Jiangtao Zhang, Dingjie Suo, Jinglong Wu, Guangying Pei, Boyan Fang, Tianyi Yan
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.
{"title":"Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms","authors":"Zhongyan Shi, Lei Ding, Xingyu Han, Bo Jiang, Jiangtao Zhang, Dingjie Suo, Jinglong Wu, Guangying Pei, Boyan Fang, Tianyi Yan","doi":"10.26599/BSA.2023.9050015","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050015","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49075938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.26599/BSA.2023.9050016
Xiaopeng Si, Dong Huang, Yulin Sun, Shudi Huang, He Huang, Dong Ming
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 restricted. To counter these issues, the 2022 World Robot Contest successfully held an affective BCI competition, thus promoting the innovation of EEG-based emotion recognition. In this paper, we propose the Transformer-based ensemble (TBEM) deep learning model. TBEM comprises two models: a pure convolutional neural network (CNN) model and a cascaded CNN-Transformer hybrid model. The 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.
{"title":"Transformer-based ensemble deep learning model for EEG-based emotion recognition","authors":"Xiaopeng Si, Dong Huang, Yulin Sun, Shudi Huang, He Huang, Dong Ming","doi":"10.26599/BSA.2023.9050016","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050016","url":null,"abstract":"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 restricted. To counter these issues, the 2022 World Robot Contest successfully held an affective BCI competition, thus promoting the innovation of EEG-based emotion recognition. In this paper, we propose the Transformer-based ensemble (TBEM) deep learning model. TBEM comprises two models: a pure convolutional neural network (CNN) model and a cascaded CNN-Transformer hybrid model. The 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.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44751864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.26599/BSA.2023.9050017
Bin Wang, Tianyi Yan
1 College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China 2 School of Life Science, Beijing Institute of Technology, Beijing 100081, China In the last two decades, neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterations in brain disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and schizophrenia (SZ), bipolar disorder (BD), etc. Multi-modal neuroimaging techniques usually includes electroencephalography (EEG), magnetic resonance imaging (MRI), magnetoencephalography (MEG), positron emission tomography (PET), near-infrared spectroscopy (NIRS). Compared with singlemodal neuroimaging technique, multi-modal neuroimaging techniques should significantly contribute to the brain working mechanisms, and promote to identify more valuable information of potential neurobiological markers, and improve the diagnosis accuracy of neurological diseases. The special session includes five papers contributed by experts who have been studying the conceptual and methodological innovations as well as practical applications of the multimodal neuroimaging techniques. Niu and his colleague [1] focused on how the network complexity changes driving spontaneous functional MRI (fMRI) activity in SZ and BD patients. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intramodule level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. This paper proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness. Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. Guan and his colleague [2] define the neural mechanisms underlying divided and selective spatial attention by fMRI and Posner paradigm with concurrent audiovisual. They explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation. Specially, the differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the
{"title":"Multi-modal neuroimaging technique: Innovations and applications","authors":"Bin Wang, Tianyi Yan","doi":"10.26599/BSA.2023.9050017","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050017","url":null,"abstract":"1 College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China 2 School of Life Science, Beijing Institute of Technology, Beijing 100081, China In the last two decades, neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterations in brain disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and schizophrenia (SZ), bipolar disorder (BD), etc. Multi-modal neuroimaging techniques usually includes electroencephalography (EEG), magnetic resonance imaging (MRI), magnetoencephalography (MEG), positron emission tomography (PET), near-infrared spectroscopy (NIRS). Compared with singlemodal neuroimaging technique, multi-modal neuroimaging techniques should significantly contribute to the brain working mechanisms, and promote to identify more valuable information of potential neurobiological markers, and improve the diagnosis accuracy of neurological diseases. The special session includes five papers contributed by experts who have been studying the conceptual and methodological innovations as well as practical applications of the multimodal neuroimaging techniques. Niu and his colleague [1] focused on how the network complexity changes driving spontaneous functional MRI (fMRI) activity in SZ and BD patients. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intramodule level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. This paper proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness. Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. Guan and his colleague [2] define the neural mechanisms underlying divided and selective spatial attention by fMRI and Posner paradigm with concurrent audiovisual. They explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation. Specially, the differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47594131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.26599/BSA.2023.9050008
Zhongtian Guan, Meng Lin, Qiong Wu, Jinglong Wu, Kewei Chen, Hongbin Han, D. Chui, Xu Zhang, Chunlin Li
Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to devided attention. We found differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the salience network under different attention conditions. We further used Granger causality (GC) to explore effective connectivity differences between tasks. Differences in GC connectivity between visual and auditory selective tasks reflected the visual dominance effect under spatial attention. In addition, our results supported the role of the putamen in redistributing attention and the functional separation of the salience network. In summary, we explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation.
{"title":"Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception","authors":"Zhongtian Guan, Meng Lin, Qiong Wu, Jinglong Wu, Kewei Chen, Hongbin Han, D. Chui, Xu Zhang, Chunlin Li","doi":"10.26599/BSA.2023.9050008","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050008","url":null,"abstract":"Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to devided attention. We found differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the salience network under different attention conditions. We further used Granger causality (GC) to explore effective connectivity differences between tasks. Differences in GC connectivity between visual and auditory selective tasks reflected the visual dominance effect under spatial attention. In addition, our results supported the role of the putamen in redistributing attention and the functional separation of the salience network. In summary, we explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48345525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.26599/BSA.2023.9050014
Bin Wang, Lan Yang, Wenjie Yan, Weichao An, Jie Xiang, Dandan Li
Brain asymmetry, involving structural and functional differences between the two hemispheres, is a major organizational principle of the human brain. The structural and functional connectivity within each hemisphere defines the hemispheric network or connectome. Elucidating left-right differences of the hemispheric network provides opportunities for brain asymmetry exploration. This review examines the asymmetry in the hemispheric white matter and functional network to assess health and brain disorders. In this article, the brain asymmetry in structural and functional connectivity including network topologies of healthy individuals, involving brain cognitive systems and the development trend, is highlighted. Moreover, the abnormal asymmetry of the hemispheric network related to cognition changes in brain disorders, such as Alzheimer’s disease, schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and bipolar disorder, is presented. This review suggests that the hemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitive system and brain disorders.
{"title":"Brain asymmetry: a novel perspective on hemispheric network","authors":"Bin Wang, Lan Yang, Wenjie Yan, Weichao An, Jie Xiang, Dandan Li","doi":"10.26599/BSA.2023.9050014","DOIUrl":"https://doi.org/10.26599/BSA.2023.9050014","url":null,"abstract":"Brain asymmetry, involving structural and functional differences between the two hemispheres, is a major organizational principle of the human brain. The structural and functional connectivity within each hemisphere defines the hemispheric network or connectome. Elucidating left-right differences of the hemispheric network provides opportunities for brain asymmetry exploration. This review examines the asymmetry in the hemispheric white matter and functional network to assess health and brain disorders. In this article, the brain asymmetry in structural and functional connectivity including network topologies of healthy individuals, involving brain cognitive systems and the development trend, is highlighted. Moreover, the abnormal asymmetry of the hemispheric network related to cognition changes in brain disorders, such as Alzheimer’s disease, schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and bipolar disorder, is presented. This review suggests that the hemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitive system and brain disorders.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47768989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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