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学者姓名：黄志华
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Abstract ：
In clinical EEG research, the construction of EEG datasets has an important impact on the research results. However, the needs of dataset construction are diverse, each of which involves multiple dimensions, and a single construction scheme cannot meet the needs of users. Meanwhile, when extracting samples, labels and requirements need to be compared one by one, and most datasets do not provide negative examples. Based on the above problems, a set of rules is proposed to describe the requirements of constructing EEG datasets, and a software system framework supporting rule-driven EEG dataset generation is designed and implemented. It solves the problem of too rigid rule making and lack of negative example labels when constructing the data set. It makes the construction of data sets more convenient and efficient. Finally, based on the system, a negative example construction scheme of active learning based on uncertain query strategy was designed. The model trained using the dataset constructed by the proposed scheme has better performance compared to the scheme where the negative examples are randomly selected. © 2023 IEEE.
Keyword ：
Clinical research Clinical research Computer software Computer software Electroencephalography Electroencephalography Electrophysiology Electrophysiology Query processing Query processing
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| GB/T 7714 | Huang, Zexin , Han, Liyong , Huang, Zhihua et al. Automated data set construction system for clinical EEG research [C] . 2023 . |
| MLA | Huang, Zexin et al. "Automated data set construction system for clinical EEG research" . (2023) . |
| APA | Huang, Zexin , Han, Liyong , Huang, Zhihua , Lin, Zhixiong , Wang, Chenghua . Automated data set construction system for clinical EEG research . (2023) . |
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Automatic sleep staging plays an essential role in the diagnosis of sleep disorders. While significant advancements have been achieved in the field of automatic sleep staging research, three challenges persist: (1) The problem of how to effectively extract and utilize the features of multi-channel physiological signals has not been well addressed. (2) The temporal correlation of sleep signals is very strong, so how to design the network to learn the correlation between local temporal features becomes significant. (3) Due to there are many differences in physiological signals between different individuals, this difference seriously affects the generalization of model training methods. To address the aforementioned challenges, the Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification (CT-DAL) is proposed. This method enables automated extraction of spatio-temporal features from multi-channel signals and utilizes multi-head attention to learn the correlations between these diverse features. Finally, by integrating Domain Adversarial Learning into the network, the common features between different individuals can be learned to enhance model's generalization capability. In this study, the efficacy of each module is demonstrated through ablation experiments, and the experimental results from baseline comparisons provide compelling evidence that the proposed model outperforms all baseline models. © 2023 IEEE.
Keyword ：
Biomedical signal processing Biomedical signal processing Convolution Convolution Deep learning Deep learning Learning systems Learning systems Physiological models Physiological models Physiology Physiology Sleep research Sleep research
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| GB/T 7714 | Wang, Yanping , Mei, Zhen , Wu, Qikai et al. Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification [C] . 2023 . |
| MLA | Wang, Yanping et al. "Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification" . (2023) . |
| APA | Wang, Yanping , Mei, Zhen , Wu, Qikai , Huang, Zhihua . Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification . (2023) . |
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The cognitive decision-making process in the ultimatum game (UG) paradigm consists of three stages: 1) option evaluation stage; 2) action selection and execution stage; 3) outcome evaluation stage. EEG correlation between different stages of UG is an important issue in exploring cognitive decision-making process. However, the ERP-based analysis is not sufficient to reveal EEG correlations at different stages. This study proposes an EEG time-frequency correlation analysis method. This method first calculates Event-Related Spectral Perturbations (ERSPs), and then uses non-parametric statistical tests with the Pearson correlation coefficient as the first-order statistical test quantity to calculate the EEG correlates of different decision stages in different decision states. It was found that there is a correlation between the first and second stages of the decision-making process when accepting a decision. These findings support the division of the decision-making process and indicate differences in different decision states, demonstrating that ERSP is a useful tool for decision analysis. © 2023 IEEE.
Keyword ：
Correlation methods Correlation methods Decision making Decision making Statistical tests Statistical tests
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| GB/T 7714 | Jin, Zhan , Gao, Xiang , Huang, Zhihua . EEG time-frequency correlation analysis of different decision stages in the ultimatum game [C] . 2023 . |
| MLA | Jin, Zhan et al. "EEG time-frequency correlation analysis of different decision stages in the ultimatum game" . (2023) . |
| APA | Jin, Zhan , Gao, Xiang , Huang, Zhihua . EEG time-frequency correlation analysis of different decision stages in the ultimatum game . (2023) . |
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In recent decades, many brain-computer interface (BCI) software platforms have emerged. However, there are still some limitations. First, integrating an algorithm on online BCI software platform is difficult and time-consuming. Second, there is no guarantee that an unproven algorithm will work properly. Last, existing platforms do not support automatic recording of result metrics during experiments, and frequent validation of multiple algorithms is inefficient. In this paper, we designed a novel BCI experiment simulation platform (BESP) based on offline analysis to satisfy the needs of algorithm validation. BESP reorganizes the experimental steps and provides some interfaces, which makes algorithm validation simple and efficient. In addition, BESP is able to record the results produced during the experimentation. Then the results of multiple algorithms can be easily analyzed, compared and visualized by the statistical analysis system provided by BESP. With these characteristics, BESP is able to verify the correctness of the algorithm. Finally, we designed a simulated P300 spelling experiment based on BESP, and completed the validation and analysis of Algorithms SPLUCB and TSPLUCB (based on PLUCB and TPLUCB). Furthermore, we used BESP to analyze the differences between the stimulus signals of the 20 subjects. Through the analysis, we found some areas worthy of further study. In summary, BESP is well suited for the process of analysis, optimization and validation among multiple algorithms. © 2023 IEEE.
Keyword ：
Brain computer interface Brain computer interface Simulation platform Simulation platform
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| GB/T 7714 | Chen, Qingzhi , Ke, Jingshuang , Huang, Zhihua . BESP: A novel BCI experiment simulation platform to assist BCI-related algorithm validation [C] . 2023 . |
| MLA | Chen, Qingzhi et al. "BESP: A novel BCI experiment simulation platform to assist BCI-related algorithm validation" . (2023) . |
| APA | Chen, Qingzhi , Ke, Jingshuang , Huang, Zhihua . BESP: A novel BCI experiment simulation platform to assist BCI-related algorithm validation . (2023) . |
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Clinical electroencephalography (EEG) plays a crucial role in the research, diagnosis and treatment of brain diseases. Accurate EEG annotations are particularly essential for these purposes. Existing EEG annotation tools are developed for specific research tasks, restricting their applicability. To address this issue, we have designed and developed a convenient and efficient system for EEG annotation. This system supports both channel-specific waveform annotation and channel-independent state annotation with real-time visualization effect. For these two types of annotation, we offer commonly used labels to meet the needs of diverse studies. Additionally, the system provides data conversion and preprocessing capability, various visualization settings and sample browsing functions, enhancing the user experience. The application of this system can accelerate the establishment of high-quality, multi-class annotation datasets and facilitate brain disease research. © 2023 IEEE.
Keyword ：
Clinical research Clinical research Data handling Data handling Data visualization Data visualization Electroencephalography Electroencephalography Electrophysiology Electrophysiology Visualization Visualization
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| GB/T 7714 | Zhao, Youwen , Lin, Zhixiong , Wang, Chenghua et al. An EEG annotation system facilitating brain disease research [C] . 2023 . |
| MLA | Zhao, Youwen et al. "An EEG annotation system facilitating brain disease research" . (2023) . |
| APA | Zhao, Youwen , Lin, Zhixiong , Wang, Chenghua , Li, Jie , Huang, Zhihua . An EEG annotation system facilitating brain disease research . (2023) . |
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Under certain task conditions, error-related potential (ErrP) will be elicited, meaning that the subject is perceiving an error, responding to an external error, or engaging in a cognitive process of reinforcement learning. The detection of ErrP on a single trial basis has been studied and applied to improve all kinds of brain-computer interfaces (BCIs). However, the performance of this kind of detection is not currently good enough. In the paper, we proposed a novel method, called window-adjusted common spatial pattern (WACSP), for detecting ErrP in P300 BCI. In this method, the coefficient of determination was introduced to measure the difference of Electroencephalogram (EEG) signals on a channel at a moment and to guide the search of time windows in which EEG differences are significant, and common spatial pattern (CSP) was further used to capture the stable spatial patterns of EEG differences between correct and incorrect responses in each time window. WACSP and the commonly used methods were tested on the data sets that were built using the EEG signals acquired during the P300 BCI experiments with different feedback. The comparisons of accuracy, area under receiver operating characteristics curve (AUC) and F-measure show that WACSP significantly outperforms the commonly used methods. The proposed method can improve ErrP detection based on a single trial.
Keyword ：
Brain-computer interface Brain-computer interface Error-related potentials Error-related potentials Window-adjusted common spatial pattern Window-adjusted common spatial pattern
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| GB/T 7714 | Huang, Zhihua , Li, Minghong , Zheng, Wenming et al. Window-Adjusted Common Spatial Pattern for Detecting Error-Related Potentials in P300 BCI [J]. | NEURAL PROCESSING LETTERS , 2023 , 55 (8) : 10829-10844 . |
| MLA | Huang, Zhihua et al. "Window-Adjusted Common Spatial Pattern for Detecting Error-Related Potentials in P300 BCI" . | NEURAL PROCESSING LETTERS 55 . 8 (2023) : 10829-10844 . |
| APA | Huang, Zhihua , Li, Minghong , Zheng, Wenming , Wu, Yingjie , Jiang, Kun , Zheng, Huiru . Window-Adjusted Common Spatial Pattern for Detecting Error-Related Potentials in P300 BCI . | NEURAL PROCESSING LETTERS , 2023 , 55 (8) , 10829-10844 . |
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Abstract ：
本发明涉及一种实现无校准P300脑机接口的方法，该方法为每个被试设置两个指示器，一个指示器表示当前刺激诱发了P300成分，另一个指示器表示当前刺激没有诱发P300成分；在推进过程中，在观测当前脑电信号的基础上依据设定的策略选取一个指示器作为对当前刺激是否诱发P300成分的判断；同时在P300脑机接口实验平台上，根据平台的反馈优化选取策略。该方法可以在无校准的前提下启动P300脑机接口交互，并在交互过程中快速优化性能，提高了P300脑机接口的工作效率。
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| GB/T 7714 | 黄志华 . 一种实现无校准P300脑机接口的方法 : CN202111616160.5[P]. | 2021-12-27 00:00:00 . |
| MLA | 黄志华 . "一种实现无校准P300脑机接口的方法" : CN202111616160.5. | 2021-12-27 00:00:00 . |
| APA | 黄志华 . 一种实现无校准P300脑机接口的方法 : CN202111616160.5. | 2021-12-27 00:00:00 . |
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Decision-making is a very important cognitive process in our daily life. There has been increasing interest in the discriminability of single-trial electroencephalogram (EEG) during decision-making. In this study, we designed a machine learning based framework to explore the discriminability of single-trial EEG corresponding to different decisions. For each subject, the framework split the decision-making trials into two parts, trained a feature model and a classifier on the first part, and evaluated the discriminability on the second part using the feature model and classifier. A proposed algorithm and five existing algorithms were applied to fulfill the feature models, and the algorithm Linear Discriminative Analysis (LDA) was used to implement the classifiers. We recruited 21 subjects to participate in Chicken Game (CG) experiments. The results show that there exists the discriminability of single-trial EEG between the cooperation and aggression decisions during the CG experiments, with the classification accuray of 75% (+/- 6%), and the discriminability is mainly from the EEG information below 40 Hz. The further analysis indicates that the contributions of different brain regions to the discriminability are consistent with the existing knowledge on the cognitive mechanism of decision-making, confirming the reliability of the conclusions. This study exhibits that it is feasible to apply machine learning methods to EEG analysis of decision-making cognitive process.
Keyword ：
Adaptive frequency common spatial pattern Adaptive frequency common spatial pattern Chicken game Chicken game Decision-making Decision-making Discriminability of single-trial EEG Discriminability of single-trial EEG
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| GB/T 7714 | Huang, Zhihua , Jiang, Kun , Li, Jing et al. Discriminability of single-trial EEG during decision-making of cooperation or aggression: a study based on machine learning [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2022 , 60 (8) : 2217-2227 . |
| MLA | Huang, Zhihua et al. "Discriminability of single-trial EEG during decision-making of cooperation or aggression: a study based on machine learning" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 60 . 8 (2022) : 2217-2227 . |
| APA | Huang, Zhihua , Jiang, Kun , Li, Jing , Zhu, Wenxing , Zheng, Huiru , Wang, Yiwen . Discriminability of single-trial EEG during decision-making of cooperation or aggression: a study based on machine learning . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2022 , 60 (8) , 2217-2227 . |
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Introduction: As a direct bridge between the brain and the outer world, brain-computer interface (BCI) is expected to replace, restore, enhance, supplement, or improve the natural output of brain. The prospect of BCI serving humans is very broad. However, the extensive applications of BCI have not been fully achieved. One of reasons is that the cost of calibration reduces the convenience and usability of BCI. Methods: In this study, we proposed a calibration-free approach, which is based on the ideas of reinforcement learning and transfer learning, for P300-based BCI. This approach, composed of two algorithms: P300 linear upper confidence bound (PLUCB) and transferred PLUCB (TPLUCB), is able to learn during the usage by exploration and exploitation and allows P300-based BCI to start working without any calibration. Results: We tested the performances of PLUCB and TPLUCB using stepwise linear discriminant analysis (SWLDA), a commonly used method that needs calibration, as a baseline in simulated online experiments. The results showed the merits of PLUCB and TPLUCB. PLUCB can quickly increase the accuracies to the level of SWLDA. TPLUCB has surpassed SWLDA in the sample accuracy since it starts running. Both PLUCB and TPLUCB have the ability to keep improving the classification performance during the process. The overall sample accuracies (73.6 +/- 4.8%, 73.1 +/- 4.9%), overall symbol accuracies (80.4 +/- 12.8%, 79.6 +/- 14.0%), F-measures (0.45 +/- 0.06, 0.44 +/- 0.06) and information transfer ratios (ITR) (36.4 +/- 9.1, 35.5 +/- 9.8) of PLUCB and TPLUCB are significantly better than those of SWLDA (overall sample accuracy: 58.8 +/- 3.8%, overall symbol accuracy: 69.0 +/- 18.3%, F-measure: 0.38 +/- 0.04, ITR: 28.7 +/- 10.7). Conclusions: The proposed approach, which does not need calibration but outperform SWLDA, is a very good option for the implementation of P300-based BCI.
Keyword ：
Calibration-free Calibration-free P300 BCI P300 BCI Reinforcement learning Reinforcement learning Transfer learning Transfer learning
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| GB/T 7714 | Huang, Zhihua , Guo, Jiannan , Zheng, Wenming et al. A Calibration-free Approach to Implementing P300-based Brain-computer Interface [J]. | COGNITIVE COMPUTATION , 2022 , 14 (2) : 887-899 . |
| MLA | Huang, Zhihua et al. "A Calibration-free Approach to Implementing P300-based Brain-computer Interface" . | COGNITIVE COMPUTATION 14 . 2 (2022) : 887-899 . |
| APA | Huang, Zhihua , Guo, Jiannan , Zheng, Wenming , Wu, Yingjie , Lin, Zhixiong , Zheng, Huiru . A Calibration-free Approach to Implementing P300-based Brain-computer Interface . | COGNITIVE COMPUTATION , 2022 , 14 (2) , 887-899 . |
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Chronic disorders of consciousness (DOC) refers to brain damage caused by various reasons, resulting in the reduction or loss of patients' ability to perceive the stimuli from the environment and themselves. DOC includes vegetative state / unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). Many researchers have done a lot of research on the automatic classification of VS and MCS patients. In this study, we proposed an automatic state classification method based on machine learning. Firstly, the EEG signal is extracted by feature measurement methods such as time domain, frequency domain, time-frequency domain, and nonlinear analysis, and a total of 34 kinds of the abovementioned features are extracted. Then an eXtreme Gradient Boosting (XGBoost) classifier is established based on the extracted feature vectors and applied to the collected dataset for state classification. The data set in this paper uses the EEG data of 12 patients (including DOC and normal state) collected by Fujian Sanbo Funeng Brain Hospital for experiments to verify the feasibility and effectiveness of the proposed method. The experimental results show that the classification accuracy of the proposed method for VS, MCS, and Normal state patients is 99.91%. © 2022 IEEE.
Keyword ：
Biomedical signal processing Biomedical signal processing Classification (of information) Classification (of information) Frequency domain analysis Frequency domain analysis Nonlinear analysis Nonlinear analysis Time domain analysis Time domain analysis
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| GB/T 7714 | An, Junjie , Weng, Chaoqun , Wang, Chenghua et al. Recognizing the consciousness states of DOC patients by classifying EEG signal [C] . 2022 . |
| MLA | An, Junjie et al. "Recognizing the consciousness states of DOC patients by classifying EEG signal" . (2022) . |
| APA | An, Junjie , Weng, Chaoqun , Wang, Chenghua , Huang, Zhihua . Recognizing the consciousness states of DOC patients by classifying EEG signal . (2022) . |
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