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Junhua Li

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6 papers

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JBHI Journal 2026 Journal Article

Prediction Consistency and Confidence-Based Proxy Domain Construction for Privacy-Preserving in Cross-Subject EEG Classification

Yong Peng
Jiangchuan Liu
Honggang Liu
Natasha Padfield
Junhua Li
Wanzeng Kong
Bao-Liang Lu
Andrzej Cichocki

Domainadaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects while only unlabeled data is provided for target subjects. Existing domain adaptation methods typically reduced the distribution discrepancy between source and target domains by directly utilizing source domain samples or features. To safeguard the privacy of source domain data, we propose to construct a Proxy Domain by simultaneously considering the prediction Consistency and Confidence (PDCC) of locally trained source models on target EEG samples, serving as the substitute to the source domain. The framework commences with the augmentation and alignment of the source domain data to enhance feature generalizability, after which source models are trained independently on each source subject’s data in a decentralized manner. Knowledge transfer from source to target domains is achieved exclusively through accessing to the source domain model, enabling the PDCC-based proxy domain construction that encapsulates the source knowledge. Finally, domain adaptation is performed using the proxy domain and target domain. As a result, PDCC eliminates the need to access source domain data while effectively leveraging source knowledge. Experimental results on four benchmark EEG datasets demonstrate that PDCC consistently outperforms eleven existing methods, including several advanced transfer learning and source-free methods. Especially, the effectiveness of the proxy domain is extensively investigated.

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JBHI Journal 2025 Journal Article

AdaptEEG: A Deep Subdomain Adaptation Network With Class Confusion Loss for Cross-Subject Mental Workload Classification

Wu Sun
Junhua Li

EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation techniques have shown effectiveness in enhancing the accuracy of cross-subject classification. However, current state-of-the-art methods for cross-subject mental workload classification primarily focus on global domain adaptation, which may lack fine-grained information and result in ambiguous classification boundaries. We proposed a novel approach called deep subdomain adaptation network with class confusion loss (DSAN-CCL) to enhance the performance of cross-subject mental workload classification. DSAN-CCL utilizes the local maximum mean discrepancy to align the feature distributions between the source domain and the target domain for each mental workload category. Moreover, the class confusion matrix was constructed by the product of the weighted class probabilities (class probabilities predicted by the label classifier) and the transpose of the class probabilities. The loss for maximizing diagonal elements and minimizing non-diagonal elements of the class confusion matrix was added to increase the credibility of pseudo-labels, thus improving the transfer performance. The proposed DSAN-CCL method was validated on two datasets, and the results indicate a significant improvement of 3∼10 percentage points compared to state-of-the-art domain adaptation methods. In addition, our proposed method is not dependent on a specific feature extractor. It can be replaced by any other feature extractor to fit new applications. This makes our approach universal to cross-domain classification problems.

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JBHI Journal 2024 Journal Article

Driving Fatigue Detection Based on Hybrid Electroencephalography and Eye Tracking

Zequan Lian
Tao Xu
Zhen Yuan
Junhua Li
Nitish Thakor
Hongtao Wang

EEG-based unimodal method has demonstrated significant success in the detection of driving fatigue. Nonetheless, data from a single modality might be not sufficient to optimize fatigue detection due to incomplete information. To address this limitation and enhance the performance of driving fatigue detection, a novel multimodal architecture combining hybrid electroencephalograph (EEG) and eye tracking data was proposed in this work. Specifically, the EEG and eye tracking data were separately input into encoders, generating two one-dimensional (1D) features. Subsequently, these 1D features were fed into a cross-modal predictive alignment module to improve fusion efficiency and two 1D attention modules to enhance feature representation. Furthermore, the fused features were recognized by a linear classifier. To evaluate the effectiveness of the proposed multimodal method, comprehensive validation tasks were conducted, including intra-session, cross-session, and cross-subject evaluations. In the intra-session task, the proposed architecture achieves an exceptional average accuracy of 99. 93%. Moreover, in the cross-session task, our method demonstrates an average accuracy of 88. 67%, surpassing the performance of EEG-only approach by 8. 52%, eye tracking-only method by 5. 92%, multimodal deep canonical correlation analysis (DCCA) technique by 0. 42%, and multimodal deep generalized canonical correlation analysis (DGCCA) approach by 0. 84%. Similarly, in the cross-subject task, the proposed approach achieves an average accuracy of 78. 19%, outperforming EEG-only method by 5. 87%, eye tracking-only approach by 4. 21%, DCCA method by 0. 55%, and DGCCA approach by 0. 44%. The experimental results conclusively illustrate the superior effectiveness of the proposed method compared to both single modality approaches and canonical correlation analysis-based multimodal methods.

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JBHI Journal 2017 Journal Article

Canonical Polyadic Decomposition With Auxiliary Information for Brain–Computer Interface

Junhua Li
Chao Li
Andrzej Cichocki

Physiological signals are often organized in the form of multiple dimensions (e. g. , channel, time, task, and 3-D voxel), so it is better to preserve original organization structure when processing. Unlike vector-based methods that destroy data structure, canonical polyadic decomposition (CPD) aims to process physiological signals in the form of multiway array, which considers relationships between dimensions and preserves structure information contained by the physiological signal. Nowadays, CPD is utilized as an unsupervised method for feature extraction in a classification problem. After that, a classifier, such as support vector machine, is required to classify those features. In this manner, classification task is achieved in two isolated steps. We proposed supervised CPD by directly incorporating auxiliary label information during decomposition, by which a classification task can be achieved without an extra step of classifier training. The proposed method merges the decomposition and classifier learning together, so it reduces procedure of classification task compared with that of respective decomposition and classification. In order to evaluate the performance of the proposed method, three different kinds of signals, synthetic signal, EEG signal, and MEG signal, were used. The results based on evaluations of synthetic and real signals demonstrated that the proposed method is effective and efficient.

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YNIMG Journal 2017 Journal Article

The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study

Yu Sun
Julian Lim
Zhongxiang Dai
KianFoong Wong
Fumihiko Taya
Yu Chen
Junhua Li
Nitish Thakor

Although rest breaks are commonly administered as a countermeasure to reduce mental fatigue and boost cognitive performance, the effects of taking a break on behavior are not consistent. Moreover, our understanding of the underlying neural mechanisms of rest breaks and how they modulate mental fatigue is still rudimentary. In this study, we investigated the effects of receiving a rest break on the topological properties of brain connectivity networks via a two-session experimental paradigm, in which one session comprised four successive blocks of a mentally demanding visual selective attention task (No-rest session), whereas the other contained a rest break between the second and third task blocks (Rest session). Functional brain networks were constructed using resting-state functional MRI data recorded from 20 healthy adults before and after the performance of the task blocks. Behaviorally, subjects displayed robust time-on-task (TOT) declines, as reflected by increasingly slower reaction time as the test progressed and lower post-task self-reported ratings of engagement. However, we did not find a significant effect on task performance due to administering a mid-task break. Compared to pre-task measurements, post-task functional brain networks demonstrated an overall decrease of optimal small-world properties together with lower global efficiency. Specifically, we found TOT-related reduced nodal efficiency in brain regions that mainly resided in the subcortical areas. More interestingly, a significant block-by-session interaction was revealed in local efficiency, attributing to a significant post-task decline in No-rest session and a preserved local efficiency when a mid-task break opportunity was introduced in the Rest session. Taken together, these findings augment our understanding of how the resting brain reorganizes following the accumulation of prolonged task, suggest dissociable processes between the neural mechanisms of fatigue and recovery, and provide some of the first quantitative insights into the cognitive neuroscience of work and rest.

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AAAI Conference 2015 Conference Paper

Multi-tensor Completion with Common Structures

Chao Li
Qibin Zhao
Junhua Li
Andrzej Cichocki
Lili Guo

In multi-data learning, it is usually assumed that common latent factors exist among multi-datasets, but it may lead to deteriorated performance when datasets are heterogeneous and unbalanced. In this paper, we propose a novel common structure for multi-data learning. Instead of common latent factors, we assume that datasets share Common Adjacency Graph (CAG) structure, which is more robust to heterogeneity and unbalance of datasets. Furthermore, we utilize CAG structure to develop a new method for multi-tensor completion, which exploits the common structure in datasets to improve the completion performance. Numerical results demonstrate that the proposed method not only outperforms state-of-the-art methods for video in-painting, but also can recover missing data well even in cases that conventional methods are not applicable.

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