Pub Date : 2020-09-01DOI: 10.26599/BSA.2020.9050027
Li Hu, Zhiguo Zhang
1 CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China 2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China 3 School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518000, Guangdong, China Electroencephalogram (EEG) is an important technique for measuring population‐level electrical activity arising from the human brain. Due to its exquisite temporal sensitivity and implementation simplicity, EEG has been widely applied to dynamically evaluate the function of the brain. Being responded to a specific sensory, cognitive, or motor event, the changes of EEG signals give rise to evoked potentials (EPs) and event‐related potentials (ERPs), which are highly associated with different brain functions, e.g., perception, emotion, and cognition. These advances make the EEG technique popularly used in various basic and clinical applications. To make full use of the EEG technique, signal processing and machine learning methods are crucial in the extraction of information for better understan‐ ding the cerebral functioning. Particularly, in this age of artificial intelligence (AI), rapidly developed AI methods, such as convolutional neural networks and recurrent neural networks, have been applied to EEG signals and have achieved promising performance in many real applications. As a consequence, the field of EEG signal processing has undergone significant growth in the last few years, and the scope and range of practical applications of EEG, such as brain–computer interface (BCI), are steadily increasing. For this reason, the special issue aims to provide a collection of papers discussing the conceptual and methodological innovations as well as practical applications of the EEG techniques. This special session has included seven review papers contributed by experts in this interdisciplinary field, and all authors have worked in the fields of EEG processing methods and applications for many years. First of all, Li [1] shared his insightful and constructive thoughts on EEG signal analysis and classification. Specifically, he focused on several important and emerging topics in EEG processing, such as brain connectivity, tensor decomposition, multi‐modality, deep learning, big data, and naturalistic experiments. These topics, particularly those AI‐related topics, are both crucial and promising for the future advancement of EEG signal analysis and classification. Next, this special issue presented several papers concerning the applications of EEG in psychology, emotion recognition, and BCI. One important and conventional application field of EEG is psychology, in which EEG has been extensively used to decode the psychological Address correspondence to Li Hu, huli@psych.ac.cn; and Zhiguo Zhang, zgzhang@szu.edu.cn
{"title":"Evolving EEG signal processing techniques in the age of artificial intelligence","authors":"Li Hu, Zhiguo Zhang","doi":"10.26599/BSA.2020.9050027","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050027","url":null,"abstract":"1 CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China 2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China 3 School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518000, Guangdong, China Electroencephalogram (EEG) is an important technique for measuring population‐level electrical activity arising from the human brain. Due to its exquisite temporal sensitivity and implementation simplicity, EEG has been widely applied to dynamically evaluate the function of the brain. Being responded to a specific sensory, cognitive, or motor event, the changes of EEG signals give rise to evoked potentials (EPs) and event‐related potentials (ERPs), which are highly associated with different brain functions, e.g., perception, emotion, and cognition. These advances make the EEG technique popularly used in various basic and clinical applications. To make full use of the EEG technique, signal processing and machine learning methods are crucial in the extraction of information for better understan‐ ding the cerebral functioning. Particularly, in this age of artificial intelligence (AI), rapidly developed AI methods, such as convolutional neural networks and recurrent neural networks, have been applied to EEG signals and have achieved promising performance in many real applications. As a consequence, the field of EEG signal processing has undergone significant growth in the last few years, and the scope and range of practical applications of EEG, such as brain–computer interface (BCI), are steadily increasing. For this reason, the special issue aims to provide a collection of papers discussing the conceptual and methodological innovations as well as practical applications of the EEG techniques. This special session has included seven review papers contributed by experts in this interdisciplinary field, and all authors have worked in the fields of EEG processing methods and applications for many years. First of all, Li [1] shared his insightful and constructive thoughts on EEG signal analysis and classification. Specifically, he focused on several important and emerging topics in EEG processing, such as brain connectivity, tensor decomposition, multi‐modality, deep learning, big data, and naturalistic experiments. These topics, particularly those AI‐related topics, are both crucial and promising for the future advancement of EEG signal analysis and classification. Next, this special issue presented several papers concerning the applications of EEG in psychology, emotion recognition, and BCI. One important and conventional application field of EEG is psychology, in which EEG has been extensively used to decode the psychological Address correspondence to Li Hu, huli@psych.ac.cn; and Zhiguo Zhang, zgzhang@szu.edu.cn","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43043440","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050019
Zhenjiang Li, Libo Zhang, Fengrui Zhang, Ruolei Gu, W. Peng, Li Hu
Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.
{"title":"Demystifying signal processing techniques to extract resting-state EEG features for psychologists","authors":"Zhenjiang Li, Libo Zhang, Fengrui Zhang, Ruolei Gu, W. Peng, Li Hu","doi":"10.26599/BSA.2020.9050019","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050019","url":null,"abstract":"Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46605413","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050018
Libo Zhang, Zhenjiang Li, Fengrui Zhang, Ruolei Gu, W. Peng, Li Hu
To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task‐related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting‐state EEG. This review aims to demystify commonly used signal processing methods for identifying task‐related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task‐related EEG and resting‐state EEG. We then discuss the methods to extract and visualize event‐related potentials in the time domain and event‐related oscillatory responses in the time‐frequency domain. Potential applications of advanced techniques such as source analysis and single‐trial analysis are briefly discussed. We conclude this review with a short summary of task‐related EEG data analysis, recommendations for further study, and caveats we should take heed of.
{"title":"Demystifying signal processing techniques to extract task-related EEG responses for psychologists","authors":"Libo Zhang, Zhenjiang Li, Fengrui Zhang, Ruolei Gu, W. Peng, Li Hu","doi":"10.26599/BSA.2020.9050018","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050018","url":null,"abstract":"To investigate neural mechanisms of human psychology with electroencephalography (EEG), we typically instruct participants to perform certain tasks with simultaneous recording of their brain activities. The identification of task‐related EEG responses requires data analysis techniques that are normally different from methods for analyzing resting‐state EEG. This review aims to demystify commonly used signal processing methods for identifying task‐related EEG activities for psychologists. To achieve this goal, we first highlight the different preprocessing pipelines between task‐related EEG and resting‐state EEG. We then discuss the methods to extract and visualize event‐related potentials in the time domain and event‐related oscillatory responses in the time‐frequency domain. Potential applications of advanced techniques such as source analysis and single‐trial analysis are briefly discussed. We conclude this review with a short summary of task‐related EEG data analysis, recommendations for further study, and caveats we should take heed of.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48557686","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050021
Rui Zhang, Fali Li, Tao Zhang, D. Yao, Peng Xu
Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.
{"title":"Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions","authors":"Rui Zhang, Fali Li, Tao Zhang, D. Yao, Peng Xu","doi":"10.26599/BSA.2020.9050021","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050021","url":null,"abstract":"Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48910081","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050020
Junhua Li
Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.
{"title":"Thoughts on neurophysiological signal analysis and classification","authors":"Junhua Li","doi":"10.26599/BSA.2020.9050020","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050020","url":null,"abstract":"Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45576622","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050026
Wanrou Hu, G. Huang, Linling Li, Li Zhang, Zhiguo Zhang, Zhen Liang
Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.
{"title":"Video‐triggered EEG‐emotion public databases and current methods: A survey","authors":"Wanrou Hu, G. Huang, Linling Li, Li Zhang, Zhiguo Zhang, Zhen Liang","doi":"10.26599/BSA.2020.9050026","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050026","url":null,"abstract":"Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43479796","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050017
Zehong Cao
The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.
{"title":"A review of artificial intelligence for EEG‐based brain−computer interfaces and applications","authors":"Zehong Cao","doi":"10.26599/BSA.2020.9050017","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050017","url":null,"abstract":"The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment, making brain–computer interface (BCI) top interdisciplinary research. Furthermore, with the modern technology advancement in artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods, there is vast growing interest in the electroencephalogram (EEG)‐based BCIs for AI‐related visual, literal, and motion applications. In this review study, the literature on mainstreams of AI for the EEG‐based BCI applications is investigated to fill gaps in the interdisciplinary BCI field. Specifically, the EEG signals and their main applications in BCI are first briefly introduced. Next, the latest AI technologies, including the ML and DL models, are presented to monitor and feedback human cognitive states. Finally, some BCI‐inspired AI applications, including computer vision, natural language processing, and robotic control applications, are presented. The future research directions of the EEG‐based BCI are highlighted in line with the AI technologies and applications.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43026236","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 : 2020-09-01DOI: 10.26599/BSA.2020.9050024
Weiwei Shi, Jinyong Zhang, Zhiguo Zhang, Lizhi Hu, Yongqian Su
Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.
{"title":"An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation","authors":"Weiwei Shi, Jinyong Zhang, Zhiguo Zhang, Lizhi Hu, Yongqian Su","doi":"10.26599/BSA.2020.9050024","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050024","url":null,"abstract":"Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45602336","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 : 2020-06-01DOI: 10.26599/BSA.2020.9050005
Gang Wu, M. Ruben, Yinyeng Lee, JiaJia Li, M. Hughes, J. Hogenesch
Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.
{"title":"Genome-wide studies of time of day in the brain: Design and analysis","authors":"Gang Wu, M. Ruben, Yinyeng Lee, JiaJia Li, M. Hughes, J. Hogenesch","doi":"10.26599/BSA.2020.9050005","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050005","url":null,"abstract":"Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43601421","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 : 2020-06-01DOI: 10.26599/BSA.2020.9050006
Michelle Werdann, Yong Zhang
The circadian clock controls daily rhythms in animal physiology, metabolism, and behavior, such as the sleep‐wake cycle. Disruption of circadian rhythms has been revealed in many diseases including neurodegenerative disorders. Interestingly, patients with many neurodegenerative diseases often show problems with circadian clocks even years before other symptoms develop. Here we review the recent studies identifying the association between circadian rhythms and several major neurodegenerative disorders. Early intervention of circadian rhythms may benefit the treatment of neurodegeneration.
{"title":"Circadian rhythm and neurodegenerative disorders","authors":"Michelle Werdann, Yong Zhang","doi":"10.26599/BSA.2020.9050006","DOIUrl":"https://doi.org/10.26599/BSA.2020.9050006","url":null,"abstract":"The circadian clock controls daily rhythms in animal physiology, metabolism, and behavior, such as the sleep‐wake cycle. Disruption of circadian rhythms has been revealed in many diseases including neurodegenerative disorders. Interestingly, patients with many neurodegenerative diseases often show problems with circadian clocks even years before other symptoms develop. Here we review the recent studies identifying the association between circadian rhythms and several major neurodegenerative disorders. Early intervention of circadian rhythms may benefit the treatment of neurodegeneration.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42414268","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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