Ziwei Wang 王紫薇

Research Vision

My long-term goal is to build reliable EEG foundation models that can better understand brain signals and support intelligent diagnosis, treatment, and rehabilitation of brain disorders.

To address this goal, I have been working on three main directions:

• Cross-species and cross-modality EEG decoding: developing transfer learning frameworks for seizure detection across species and recording modalities, including knowledge transfer from canine to human EEG and from intracranial to scalp EEG. Representative work: ResizeNet+MSA.
• Reliable EEG decoding under real-world challenges: designing robust decoding architectures and transfer learning algorithms for cross-subject, cross-dataset, low-sample, and low-SNR scenarios. Representative works: DBConformer, FAConformer, MVCNet, CKD, and TASA.
• Data foundations for brain foundation models: building knowledge-driven augmentation, denoising, and generation approaches to support data-efficient EEG modeling, pretraining, and foundation model construction. Representative works: CR, DWTaug, HHTaug, DenoNet, and DG4BCI.

News

• 06 / 2026 — CKD accepted by IEEE TBME.
• 04 / 2026 — We have released the CHSZ dataset, an EEG dataset collected from 27 children for epileptic seizure detection, for public download and use. Please refer to our TASA-SDS and CST papers for details of data processing.
• 03 / 2026 — Our survey on brain signal generation is available on arXiv. Special thanks to Tiki 🐱 for kindly providing her photo for the figures.
• 02 / 2026 — Selected for the Top 10 Advances in Brain–Computer Interfaces in China (Huanau Award).
• 12 / 2025 — Supported by the Doctoral Student Program of the Young S&T Talents Cultivation Project from CAST (40,000 RMB).
• 12 / 2025 — Supported by the Youth Student Fundamental Research Project from NSFC (300,000 RMB).
• 10 / 2025 — DBConformer accepted by IEEE JBHI.
• 09 / 2025 — Awarded the Ant Group InTech Scholarship.
• 07 / 2025 — MVCNet accepted by Knowledge-Based Systems.
• 03 / 2025 — CST accepted by National Science Review.
• 02 / 2025 — CSDA accepted by Knowledge-Based Systems.
• 08 / 2024 — Selected for the Top 10 Highlights in Brain–Computer Interfaces in China (Huanau Award).
• 04 / 2024 — CR accepted by Neural Networks.

Representative Publications

National Science Review 2025

Canine EEG helps human: Cross-species and cross-modality epileptic seizure detection via multi-space alignment

Z. Wang, S. Li, and D. Wu*

National Science Review, vol. 12, no. 6, p. nwaf086, 2025. CAS Q1 Top

A cross-species and cross-modality transfer framework for seizure detection, showing that canine EEG can help human EEG analysis.

Paper Code Media News

IEEE JBHI 2025

DBConformer: Dual-branch convolutional Transformer for EEG decoding

Z. Wang, H. Wang, T. Jia, X. He, S. Li, and D. Wu*

IEEE Journal of Biomedical and Health Informatics, vol. 30, no. 5, pp. 4134–4147, 2026. CAS Q1 Top

A dual-branch temporal-spatial model for EEG decoding that improves performance while remaining parameter-efficient.

Paper Code

IEEE TBME 2026

CKD: Contrastive knowledge distillation for cross-dataset EEG classification

Z. Wang, X. He, H. Wang, and D. Wu*

IEEE Trans. on Biomedical Engineering, Early access, 2026. DOI: 10.1109/TBME.2026.3701548. CAA-A

A two-stage contrastive knowledge distillation framework for cross-dataset EEG decoding

Paper Code

Neural Networks 2024

Channel reflection: Knowledge-driven data augmentation for EEG-based BCIs

Z. Wang†, S. Li†, J. Luo, J. Liu, and D. Wu*

Neural Networks, vol. 176, p. 106351, 2024. CAS Q1 Top

A knowledge-driven spatial augmentation strategy that leverages EEG channel symmetry to improve decoding accuracy and robustness.

Paper Code Media

Knowledge-Based Systems 2025

Time-frequency transform based EEG data augmentation for brain-computer interfaces

Z. Wang, S. Li, X. Chen, and D. Wu*

Knowledge-Based Systems, vol. 311, p. 113074, 2025. CAS Q1 Top

A time-frequency augmentation framework that improves EEG decoding performance under limited training data.

Paper Code

Knowledge-Based Systems 2025

MVCNet: Multi-view contrastive network for motor imagery classification

Z. Wang, S. Li, X. Chen, and D. Wu*

Knowledge-Based Systems, vol. 328, p. 114205, 2025. CAS Q1 Top

A multi-view contrastive learning framework designed to improve representation learning for motor imagery classification.

Paper Code

Journal of Neural Engineering 2023

Unsupervised domain adaptation for cross-patient seizure classification

Z. Wang, W. Zhang, S. Li, X. Chen, and D. Wu*

Journal of Neural Engineering, vol. 20, no. 6, p. 066002, 2023. CAS Q2 Top

An unsupervised domain adaptation framework for seizure detection across patients under distribution shifts.

Paper Code

arXiv 2026

Synthetic data generation for brain-computer interfaces: Overview, benchmarking, and future directions

Z. Wang, Z. He, X. He, H. Wang, T. Jia, J. Luo, S. Li, X. Chen, and D. Wu* CAS Q1 Top

Artificial Intelligence Review, under review, 2026.

Provide a methodological framework for brain-signal generation research.

Paper Code

arXiv 2026

FAConformer: Frequency-aware convolutional Transformer for auditory attention decoding

Z. Wang†, X. He†, T. Jia, H. Wang, and D. Wu* CAS Q1 Top

Science China Information Sciences, under review, 2026.

Improve EEG-based AAD through multi-band modeling and cross-band fusion.

Paper Code

DenoNet: A GAN-VAE hybrid generative model for SSVEP denoising

Z. He†, Z. Wang†, and D. Wu* CAS Q1 Top

Engineering, under review, 2026.

A GAN–VAE-based denoising model to improve decoding accuracy and robustness under low-SNR conditions.

Paper Code

View full publication list →

Awards

• 主持国家自然科学基金博士生项目, 2025
• 入选中国科协青年科技人才培育工程博士生专项计划, 2025
• 首届蚂蚁InTech奖学金（全球10人）, 2025
• National Scholarship (国家奖学金), PhD, 2025
• National Scholarship (国家奖学金), PhD, 2024
• National Scholarship (国家奖学金), Undergraduate, 2020
• 湖南省优秀毕业生, 2021
• 华中科技大学三好学生标兵（校学生最高荣誉）, 2025
• 中南大学特等奖学金(校最高级别奖学金), 2021
• IEEE CIS Scholarship (IEEE计算智能学会奖学金), 全球5人, 2022
• 脑机接口华瑙奖“中国脑机接口十大进展”（全国10项）, 2025
• 脑机接口华瑙奖“中国脑机接口十大亮点”（全国10项）, 2024
• 世界机器人大赛—脑控机器人大赛全国一等奖, 2023
• 世界机器人大赛—脑控机器人大赛全国二等奖, 2025
• 世界机器人大赛—脑控机器人大赛全国二等奖, 2021
• IOP Trusted Reviewer(英国物理学会可信审稿人）, 2024

Education

• 09 / 2021 – Present — Ph.D. candidate, Huazhong University of Science and Technology
• 09 / 2017 – 06 / 2021 — B.Eng. in Measurement and Control Technology and Instrumentation, Central South University

Talks

• 11 / 2023 — ICONIP Tutorial: Transfer learning for EEG-based brain–computer interfaces
• 05 / 2025 — CSSC Oral: Cross-species and cross-modality seizure detection via multi-space alignment
• 09 / 2024 — Alibaba Cloud Yunqi Conference Oral: EEG-based automatic seizure detection
• 12 / 2024 — China Brain–Computer Intelligence Conference Poster
• 12 / 2025 — SAAC 2025 Poster: DBConformer
• 12 / 2024 — SAAC 2024 Poster: CR

Internships

Alibaba Cloud, China
10 / 2022 – 04 / 2023

• Designed five augmentation operators based on N-grams and TF-IDF for anomaly-aware data augmentation.
• Proposed a SparseAttention module for long-sequence forecasting.
• Designed a domain-generalized mixture-of-experts model for robust fault prediction under temporal and device-level shifts.