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Minpeng Xu

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4 papers

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JBHI Journal 2025 Journal Article

An Online Adaptation Framework for Enhancing Calibration-Free SSVEP-Based BCI Performance

Weize Chen
Jie Mei
Xiaolin Xiao
Ang Li
Lingling Tao
Kun Wang
Minpeng Xu
Dong Ming

Accomplishing a plug-and-play steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a critical challenge, due to the unsatisfying performance of calibration-free decoding algorithms. A current method called online adaptive canonical correlation analysis (OACCA) has proved efficient in enhancing calibration-free performance by self-adaptation merely with online data. However, OACCA only concerns the adaptation of spatial filters and excludes other useful adaptive procedures like individual template estimation, hindering fully exploitable model decoding and adaptation. This study proposes a new online adaptation framework termed online adaptive extended correlation analysis (OAECA) to augment the calibration-free online adaptation loop. OAECA first recalls and cleans the online trials for reliable data learning, then tunes individual templates and spatial filters for complete model updating, and finally adopts extended feature matching to improve target recognition. The simulation results on two public SSVEP datasets revealed that OAECA significantly outperformed OACCA for almost all 105 subjects, and both offline and online experiments further confirmed the effectiveness of OAECA. Particularly, OAECA achieved the highest average information transfer rate (ITR) of 202. 17 bits/min in the online experiment, significantly exceeding the state-of-the-art OACCA of 177. 02 bits/min. This study enhanced the calibration-free performance through comprehensive online adaptation, hopefully advancing SSVEP-based BCIs toward practical plug-and-play real-world applications.

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JBHI Journal 2025 Journal Article

Decoding Arm Movement Direction Using Ultra-High-Density EEG

Zhen Ma
Xinyi Yang
Jiayuan Meng
Kun Wang
Minpeng Xu
Dong Ming

Detecting arm movement direction is significant for individuals with upper-limb motor disabilities to restore independent self-care abilities. It involves accurately decoding the fine movement patterns of the arm, which has become feasible using invasive brain-computer interfaces (BCIs). However, it is still a significant challenge for traditional electroencephalography (EEG) based BCIs to decode multi-directional arm movements effectively. This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in different directions. To extract discriminative features from UHD EEG, we proposed a spatial filtering method combining principal component analysis (PCA) and discriminative spatial pattern (DSP). We collected EEG signals from five healthy subjects (two left-handed and three right-handed) to verify the system's feasibility. The movement-related cortical potentials (MRCPs) showed a certain degree of separability both in waveforms and spatial patterns for arm movements in different directions. This study achieved an average classification accuracy of 63. 15 (8. 71)% for both arms (eight-class task) with a peak accuracy of 77. 24%. For the dominant arm (four-class task), we obtained an average accuracy of 75. 31 (9. 21)% with a peak accuracy of 85. 00%. For the first time, this study simultaneously decodes multi-directional movements of both arms using UHD EEG. This study provides a promising approach for detecting information about arm movement directions, which is significant for the development of BCIs.

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JBHI Journal 2025 Journal Article

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction

Baolian Shan
Haiqing Yu
Yongzhi Huang
Minpeng Xu
Dong Ming

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions. Therefore, it may be underperforming and uninterpretable for the CNNs without capturing complex spectral properties sufficiently. This study proposed a novel interpretable multi-branch architecture for spatiotemporal CNNs, namely MultiSincNet. On the one hand, the MultiSincNet could directly show the frequency boundaries using the interpretable sinc-convolution layers. On the other hand, it could extract and integrate multiple spatiotemporal features across varying spectral resolutions using parallel branches. Moreover, we also constructed a post-hoc explanation technique for multi-branch CNNs, using the first- order Taylor expansion and chain rule based on the multivariate composite function, which demonstrates the crucial spatiotemporal features learned by the proposed multi-branch spatiotemporal CNN. When combined with the optimal MultiSincNet, ShallowConvNet, DeepConvNet, and EEGWaveNet had significantly improved the subject-specific performance on most metrics. Specifically, the optimal MultiSincNet significantly increased the average accuracy, sensitivity, specificity, binary F1-score, weighted F1-score, and AUC of EEGWaveNet by about 7%, 8%, 7%, 8%, 7%, and 7%, respectively. Besides, the visualization results showed that the optimal model mainly extracts the spectral energy difference from the high gamma band focalized to specific spatial areas as the dominant spatiotemporal EEG feature.

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YNIMG Journal 2016 Journal Article

Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin

Minpeng Xu
Yihong Jia
Hongzhi Qi
Yong Hu
Feng He
Xin Zhao
Peng Zhou
Lixin Zhang

There has been a long debate about the neural mechanism of event-related potentials (ERPs). Previously, no evidence or method was apparent to validate the two competing models, the evoked model and the oscillation model. One argument is whether the pre-stimulus brain oscillation could influence the following ERP. This study carried out an innovative visual oddball task experiment to investigate the dynamic process of visual evoked potentials. A period of stable oscillations of specified dominant frequencies and initial phases, i. e. the steady-state baseline, would be induced before responses to transient stimuli of different contrasts, which could overcome the artifact problem caused by the ‘sorting’ method. The result first revealed a ‘three-period-transition’ for the generation of visual evoked potentials by an objective decomposition. The ERP almost retained the preceding oscillation during the first period, provided an unstable negative potential in the second period, and generated the N1 component in the third period. The cross term analysis showed that the evoked model couldn't be the whole explanation for the ERP generation. Furthermore, the component analysis revealed that the N1 latency was sensitive to the initial phase under the low stimulus contrast (supporting the oscillation model) but not under the high stimulus contrast (supporting the evoked model). It demonstrated that the external stimulus contrast is a significant factor deciding the explicit model for ERPs. Our method and preliminary results may help reconcile the previous, seemly contradictory findings on the ERP mechanism.

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