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学者姓名：李明爱
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Abstract ：
Background/Objectives: Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL. Methods: First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks. Results: The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods. Conclusions: Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.
Keyword ：
temporal-spatial feature extraction temporal-spatial feature extraction task incremental learning task incremental learning conditional diffusion conditional diffusion generative artificial intelligence generative artificial intelligence motor imagery EEG motor imagery EEG
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| GB/T 7714 | Yang, Yufei , Li, Mingai , Liu, Jianhang . Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG [J]. | BRAIN SCIENCES , 2025 , 15 (2) . |
| MLA | Yang, Yufei 等. "Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG" . | BRAIN SCIENCES 15 . 2 (2025) . |
| APA | Yang, Yufei , Li, Mingai , Liu, Jianhang . Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG . | BRAIN SCIENCES , 2025 , 15 (2) . |
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Abstract ：
Nonmuscle channels are able to enhance voluntary movement control or improve rehabilitation efficacy, and analyzing electroencephalogram (EEG) signals yields valuable insights into functional neural activity during motor imagery (MI). However, most existing MI-EEG datasets primarily focus on exploring more discriminative tasks to control external devices, making it difficult to meet the actual needs of motor function rehabilitation. In addition, the expensive acquisition of labeled data hinders the practical application of EEG-based rehabilitation. In this study, we aim to collect EEG signals from 13 subjects performing four MI tasks of the unilateral upper limb: arm lifting/lowering and forearm pronation/supination. Moreover, we propose a Contrastive representation learning framework with an Attention Spatiotemporal Convolutional Encoder (CASCE) for MI-EEG decoding. In the pretraining phase, the unlabeled data from background samples and label-erased samples are applied to the noise addition module and the scaling module to generate pairs of positive and negative samples. These sample pairs are input into the encoder to learn temporal and spatial information, and the encoder parameters are further adjusted by using the contrastive loss function to measure the similarity of the feature information in the projection space. During the fine-tuning phase, the transferred encoder and the classification head are specifically adapted to the labeled MI-EEG data. The CASCE framework achieves a classification accuracy of 51.58% on the refined upper limb MI dataset. In addition, CASCE outperforms state-of-the-art (SOTA) methods with accuracies of 88.51% and 90.34% on the brain-computer interface (BCI) Competition IV 2a and 2b datasets, respectively.
Keyword ：
Contrastive learning Contrastive learning Motors Motors upper limb decoding upper limb decoding Brain-computer interface (BCI) Brain-computer interface (BCI) Convolutional neural networks Convolutional neural networks contrastive learning contrastive learning Representation learning Representation learning Data mining Data mining Brain modeling Brain modeling Electroencephalography Electroencephalography electroencephalogram (EEG) electroencephalogram (EEG) Decoding Decoding Convolution Convolution motor imagery (MI) motor imagery (MI) Feature extraction Feature extraction
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| GB/T 7714 | Wang, Junhui , Li, Mingai . CASCE: A Contrastive Representation Learning Framework for Motor Imagery EEG-Based Unilateral Upper Limb Decoding [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Wang, Junhui 等. "CASCE: A Contrastive Representation Learning Framework for Motor Imagery EEG-Based Unilateral Upper Limb Decoding" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Wang, Junhui , Li, Mingai . CASCE: A Contrastive Representation Learning Framework for Motor Imagery EEG-Based Unilateral Upper Limb Decoding . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Abstract ：
Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance.
Keyword ：
Decoding Decoding Support vector machines Support vector machines Image sequences Image sequences EEG source imaging EEG source imaging Feature extraction Feature extraction motor imagery motor imagery Electroencephalography Electroencephalography Motors Motors Time-frequency analysis Time-frequency analysis Brain computer interface Brain computer interface ROI importance ROI importance separable convolution separable convolution
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| GB/T 7714 | Wang, Linlin , Li, Mingai , Xu, Dongqin et al. Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network [J]. | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2024 , 32 : 3636-3646 . |
| MLA | Wang, Linlin et al. "Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network" . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 32 (2024) : 3636-3646 . |
| APA | Wang, Linlin , Li, Mingai , Xu, Dongqin , Yang, Yufei . Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2024 , 32 , 3636-3646 . |
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Abstract ：
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.
Keyword ：
Motor imagery EEG Motor imagery EEG Mutual information theory Mutual information theory Broad learning system Broad learning system Incremental learning Incremental learning Temporal-spatial features Temporal-spatial features
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| GB/T 7714 | Yang, Yufei , Li, Mingai , Wang, Linlin . An adaptive session-incremental broad learning system for continuous motor imagery EEG classification [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2024 . |
| MLA | Yang, Yufei et al. "An adaptive session-incremental broad learning system for continuous motor imagery EEG classification" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2024) . |
| APA | Yang, Yufei , Li, Mingai , Wang, Linlin . An adaptive session-incremental broad learning system for continuous motor imagery EEG classification . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2024 . |
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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 et al. "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 ：
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 et al. "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 ：
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 et al. "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 ：
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 et al. "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 ：
本发明公开了基于D‑K分区的简化分布式偶极子模型建立与识别方法，具体包括：首先，利用不同的带通滤波器对原始MI‑EEG进行滤波，以挑选与运动想象活动相关的最优频带；然后，对挑选出的每个子带进行脑电逆变换，将头皮EEG转换为脑皮层中的偶极子；接着，获得基于神经解剖学D‑K分区的中心偶极子，以构建简化分布式偶极子模型，将大脑皮层中心偶极子的活动视为神经动力学系统，构建4D数据表达；最后，将多频带数据表达进行融合并输入至设计好的n分支并行的nB3DCNN中，从时‑频‑空三个维度进行综合特征提取与识别。本发明体现了不同频带下，偶极子在3D空间中幅值随着时间的变化，利用少量中心偶极子反映了整个大脑皮质层由运动想象引起的神经电活动。
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| GB/T 7714 | 李明爱 , 阮秭威 , 孙炎珺 . 基于D-K分区的简化分布式偶极子模型建立与识别方法 : CN202210239817.9[P]. | 2022-03-12 . |
| MLA | 李明爱 et al. "基于D-K分区的简化分布式偶极子模型建立与识别方法" : CN202210239817.9. | 2022-03-12 . |
| APA | 李明爱 , 阮秭威 , 孙炎珺 . 基于D-K分区的简化分布式偶极子模型建立与识别方法 : CN202210239817.9. | 2022-03-12 . |
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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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