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学者姓名：李明爱
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Abstract ：
In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.
Keyword ：
EEG source imaging EEG source imaging MI-task decoding MI-task decoding Pearson correlation coefficient Pearson correlation coefficient Electrode optimization Electrode optimization Common spatial patterns Common spatial patterns
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| GB/T 7714 | Dong, Yuxin , Wang, Linlin , Li, Mingai . Applying correlation analysis to electrode optimization in source domain [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (5) : 1225-1238 . |
| MLA | Dong, Yuxin 等. "Applying correlation analysis to electrode optimization in source domain" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 61 . 5 (2023) : 1225-1238 . |
| APA | Dong, Yuxin , Wang, Linlin , Li, Mingai . Applying correlation analysis to electrode optimization in source domain . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2023 , 61 (5) , 1225-1238 . |
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Abstract ：
The complex brain network consists of multiple collaborative regions, which can be activated to varying degrees by motor imagery (MI) and the induced electroencephalogram (EEG) recorded by an array of scalp electrodes is usually decoded for driving rehabilitation system. Either all channels or partially selected channels are equally applied to recognize movement intention, which may be incompatible with the individual differences of channels from different locations. In this paper, a channel importance based imaging method is proposed, denoted as CIBI. For each electrode of MI-EEG, the power over 8-30 Hz band is calculated from discrete Fourier spectrum and input to random forest algorithm (RF) to quantify its contribution, namely channel importance (CI); Then, CI is used for weighting the powers of a and b rhythms, which are interpolated to a 32 x 32 grid by using CloughTocher method respectively, generating two main band images with time-frequency-space information. In addition, a dual branch fusion convolutional neural network (DBFCNN) is developed to match with the characteristic of two MI images, realizing the extraction, fusion and classification of comprehensive features. Extensive experiments are conducted based on two public datasets with four classes of MI-EEG, the relatively higher average accuracies are obtained, and the improvements achieve 23.95% and 25.14% respectively when using channel importance, their statistical analysis are also performed by Kappa value, confusion matrix and receiver operating characteristic. Experiment results show that the personalized channel importance is helpful to enhance inter-class separability as well as the proposed method has the outstanding decoding ability for multiple MI tasks.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Keyword ：
Channel importance Channel importance Brain computer interface Brain computer interface Motor imagery Motor imagery Convolutional neural network Convolutional neural network Random forest algorithm Random forest algorithm
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| GB/T 7714 | Wang, Linlin , Li, Mingai . The quantitative application of channel importance in movement intention decoding [J]. | BIOCYBERNETICS AND BIOMEDICAL ENGINEERING , 2022 , 42 (2) : 630-645 . |
| MLA | Wang, Linlin 等. "The quantitative application of channel importance in movement intention decoding" . | BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 42 . 2 (2022) : 630-645 . |
| APA | Wang, Linlin , Li, Mingai . The quantitative application of channel importance in movement intention decoding . | BIOCYBERNETICS AND BIOMEDICAL ENGINEERING , 2022 , 42 (2) , 630-645 . |
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Abstract ：
Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time-frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial-temporal features from multi sub-bands.
Keyword ：
Convolutional neural network Convolutional neural network EEG source imaging EEG source imaging Simplified distributed dipoles model Simplified distributed dipoles model Motor imagery Motor imagery
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| GB/T 7714 | Li, Ming-ai , Ruan, Zi-wei . Decoding motor imagery with a simplified distributed dipoles model at source level [J]. | COGNITIVE NEURODYNAMICS , 2022 , 17 (2) : 445-457 . |
| MLA | Li, Ming-ai 等. "Decoding motor imagery with a simplified distributed dipoles model at source level" . | COGNITIVE NEURODYNAMICS 17 . 2 (2022) : 445-457 . |
| APA | Li, Ming-ai , Ruan, Zi-wei . Decoding motor imagery with a simplified distributed dipoles model at source level . | COGNITIVE NEURODYNAMICS , 2022 , 17 (2) , 445-457 . |
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Abstract ：
Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t-test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.
Keyword ：
Transfer learning Transfer learning Weighted alignment Weighted alignment Maximum mean discrepancy Maximum mean discrepancy Motor imagery Motor imagery Domain adaptation Domain adaptation
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| GB/T 7714 | Xu, Dong-qin , Li, Ming-ai . A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification [J]. | APPLIED INTELLIGENCE , 2022 , 53 (9) : 10766-10788 . |
| MLA | Xu, Dong-qin 等. "A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification" . | APPLIED INTELLIGENCE 53 . 9 (2022) : 10766-10788 . |
| APA | Xu, Dong-qin , Li, Ming-ai . A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification . | APPLIED INTELLIGENCE , 2022 , 53 (9) , 10766-10788 . |
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Abstract ：
Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient's sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.
Keyword ：
sleep stage classification sleep stage classification deep learning deep learning HHT HHT multimodal physiological signals multimodal physiological signals fusion networks fusion networks
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| GB/T 7714 | Duan, Lijuan , Li, Mengying , Wang, Changming et al. A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion [J]. | FRONTIERS IN HUMAN NEUROSCIENCE , 2021 , 15 . |
| MLA | Duan, Lijuan et al. "A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion" . | FRONTIERS IN HUMAN NEUROSCIENCE 15 (2021) . |
| APA | Duan, Lijuan , Li, Mengying , Wang, Changming , Qiao, Yuanhua , Wang, Zeyu , Sha, Sha et al. A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion . | FRONTIERS IN HUMAN NEUROSCIENCE , 2021 , 15 . |
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Abstract ：
Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima.The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM).The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs.Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively.The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.
Keyword ：
genetic algorithm genetic algorithm joint optimization joint optimization Motor imagery electroencephalogram Motor imagery electroencephalogram fuzzy c-means fuzzy c-means fuzzy support vector machine fuzzy support vector machine
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| GB/T 7714 | Li Ming-Ai , Wang Ruo-Tu , Wei Li-Na . Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means. [J]. | Technology and health care : official journal of the European Society for Engineering and Medicine , 2021 . |
| MLA | Li Ming-Ai et al. "Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means." . | Technology and health care : official journal of the European Society for Engineering and Medicine (2021) . |
| APA | Li Ming-Ai , Wang Ruo-Tu , Wei Li-Na . Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means. . | Technology and health care : official journal of the European Society for Engineering and Medicine , 2021 . |
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Abstract ：
The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset. © 2013 IEEE.
Keyword ：
Bionics Bionics Machine learning Machine learning Electroencephalography Electroencephalography Brain computer interface Brain computer interface Biomedical signal processing Biomedical signal processing Data mining Data mining Human computer interaction Human computer interaction
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| GB/T 7714 | Lian, Zhaoyang , Duan, Lijuan , Qiao, Yuanhua et al. The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface [J]. | IEEE Access , 2021 , 9 : 67405-67416 . |
| MLA | Lian, Zhaoyang et al. "The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface" . | IEEE Access 9 (2021) : 67405-67416 . |
| APA | Lian, Zhaoyang , Duan, Lijuan , Qiao, Yuanhua , Chen, Juncheng , Miao, Jun , Li, Mingai . The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface . | IEEE Access , 2021 , 9 , 67405-67416 . |
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Abstract ：
As an important task in scene understanding, semantic segmentation requires a large amount of computation to achieve high performance. In recent years, with the rise of autonomous systems, it is crucial to make a trade-off in terms of accuracy and speed. In this paper, we propose a novel asymmetric encoder-decoder network structure to address this problem. In the encoder, we design a Separable Asymmetric Module, which combines depth-wise separable asymmetric convolution with dilated convolution to greatly reduce computation cost while maintaining accuracy. On the other hand, an attention mechanism is also used in the decoder to further improve segmentation performance. Experimental results on CityScapes and CamVid datasets show that the proposed method can achieve a better balance between segmentation precision and speed compared with state-of-the-art semantic segmentation methods. Specifically, our model obtains mean IoU of 72.5% and 66.3% on CityScapes and CamVid test dataset, respectively, with less than 1M parameters.
Keyword ：
decoder structure decoder structure Dilated convolution Dilated convolution Depth-wise separable asymmetric convolution Depth-wise separable asymmetric convolution Semantic segmentation Semantic segmentation Attention mechanism Attention mechanism Encoder– Encoder–
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| GB/T 7714 | Wang, Kang , Yang, Jinfu , Yuan, Shuai et al. A lightweight network with attention decoder for real-time semantic segmentation [J]. | VISUAL COMPUTER , 2021 , 38 (7) : 2329-2339 . |
| MLA | Wang, Kang et al. "A lightweight network with attention decoder for real-time semantic segmentation" . | VISUAL COMPUTER 38 . 7 (2021) : 2329-2339 . |
| APA | Wang, Kang , Yang, Jinfu , Yuan, Shuai , Li, Mingai . A lightweight network with attention decoder for real-time semantic segmentation . | VISUAL COMPUTER , 2021 , 38 (7) , 2329-2339 . |
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Abstract ：
Objective. Motor imagery electroencephalography (MI-EEG) produces one of the most commonly used biosignals in intelligent rehabilitation systems. The newly developed 3D convolutional neural network (3DCNN) is gaining increasing attention for its ability to recognize MI tasks. The key to successful identification of movement intention is dependent on whether the data representation can faithfully reflect the cortical activity induced by MI. However, the present data representation, which is often generated from partial source signals with time-frequency analysis, contains incomplete information. Therefore, it would be beneficial to explore a new type of data representation using raw spatiotemporal dipole information as well as the possible development of a matching 3DCNN. Approach. Based on EEG source imaging and 3DCNN, a novel decoding method for identifying MI tasks is proposed, called ESICNND. MI-EEG is mapped to the cerebral cortex by the standardized low resolution electromagnetic tomography algorithm, and the optimal sampling points of the dipoles are selected as the time of interest to best reveal the difference between any two MI tasks. Then, the initial subject coordinate system is converted to a magnetic resonance imaging coordinate system, followed by dipole interpolation and volume down-sampling; the resulting 3D dipole amplitude matrices are merged at the selected sampling points to obtain 4D dipole feature matrices (4DDFMs). These matrices are augmented by sliding window technology and input into a 3DCNN with a cascading architecture of three modules (3M3DCNN) to perform the extraction and classification of comprehensive features. Main results. Experiments are carried out on two public datasets; the average ten-fold CV classification accuracies reach 88.73% and 96.25%, respectively, and the statistical analysis demonstrates outstanding consistency and stability. Significance. The 4DDFMs reveals the variation of cortical activation in a 3D spatial cube with a temporal dimension and matches the 3M3DCNN well, making full use of the high-resolution spatiotemporal information from all dipoles.
Keyword ：
EEG source imaging EEG source imaging 4D dipole feature matrix 4D dipole feature matrix motor imagery EEG motor imagery EEG convolutional neural network convolutional neural network data representation data representation time of interest time of interest
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| GB/T 7714 | Li, Ming-ai , Ruan, Zi-wei . A novel decoding method for motor imagery tasks with 4D data representation and 3D convolutional neural networks [J]. | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (4) . |
| MLA | Li, Ming-ai et al. "A novel decoding method for motor imagery tasks with 4D data representation and 3D convolutional neural networks" . | JOURNAL OF NEURAL ENGINEERING 18 . 4 (2021) . |
| APA | Li, Ming-ai , Ruan, Zi-wei . A novel decoding method for motor imagery tasks with 4D data representation and 3D convolutional neural networks . | JOURNAL OF NEURAL ENGINEERING , 2021 , 18 (4) . |
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Abstract ：
A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an active brain computer interface (BCI) system, and it has been a popular field to recognize MI-EEG images via convolutional neural network (CNN), which poses a potential problem for maintaining the integrity of the time-frequency-space information in MI-EEG images and exploring the feature fusion mechanism in the CNN. However, information is excessively compressed in the present MI-EEG image, and the sequential CNN is unfavorable for the comprehensive utilization of local features. In this paper, a multidimensional MI-EEG imaging method is proposed, which is based on time-frequency analysis and the Clough-Tocher (CT) interpolation algorithm. The time-frequency matrix of each electrode is generated via continuous wavelet transform (WT), and the relevant section of frequency is extracted and divided into nine submatrices, the longitudinal sums and lengths of which are calculated along the directions of frequency and time successively to produce a 3 x 3 feature matrix for each electrode. Then, feature matrix of each electrode is interpolated to coincide with their corresponding coordinates, thereby yielding a WT-based multidimensional image, called WTMI. Meanwhile, a multilevel and multiscale feature fusion convolutional neural network (MLMSFFCNN) is designed for WTMI, which has dense information, low signal-to-noise ratio, and strong spatial distribution. Extensive experiments are conducted on the BCI Competition IV 2a and 2b datasets, and accuracies of 92.95% and 97.03% are yielded based on 10-fold cross-validation, respectively, which exceed those of the state-of-the-art imaging methods. The kappa values and p values demonstrate that our method has lower class skew and error costs. The experimental results demonstrate that WTMI can fully represent the time-frequency-space features of MI-EEG and that MLMSFFCNN is beneficial for improving the collection of multiscale features and the fusion recognition of general and abstract features for WTMI.
Keyword ：
Wavelet transform Wavelet transform Brain-computer interface Brain-computer interface Machine learning Machine learning MI-EEG imaging method MI-EEG imaging method Convolutional neural network Convolutional neural network
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| GB/T 7714 | Li, Ming-ai , Han, Jian-fu , Yang, Jin-fu . Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2021 , 59 (10) : 2037-2050 . |
| MLA | Li, Ming-ai et al. "Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 59 . 10 (2021) : 2037-2050 . |
| APA | Li, Ming-ai , Han, Jian-fu , Yang, Jin-fu . Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2021 , 59 (10) , 2037-2050 . |
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