Publications

Chu, V., Zhang, G., Li, Q., Radulovich, N., Tsao, M., He, H., & Wang, B. (2024, July). Characterization and Profiling of Neoantigens Identified in Patient-Derived Tumor Organoids Using a Peptide-Based Transformer Model. Presentation at the ISMB 2024 Conference, Computational and Systems Immunology Track, C-075.

Coles, N. A., Perz, B., Behnke, M., Eichstaedt, J., Kim, S. H., Vu, T. N., Raman, C., Tejada, J., Zhang, G., Cui, T., & others. (2024). Big Team Science Reveals Promises and Limitations of Machine Learning Efforts to Model the Physiological Basis of Affective Experience. OSF. https://doi.org/10.31234/osf.io/j9gbx. Manuscript submitted to Nature Human Behaviour. 

Zhang, G., & Etemad, A. (2024). Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(2), 1469-1483. https://doi.org/10.1109/TETCI.2023.3332549

Zhang, G., & Etemad, A. (2023). Partial Label Learning for Emotion Recognition from EEG. arXiv preprint arXiv:2302.13170. https://doi.org/10.48550/arXiv.2302.13170. Manuscript revised and under review at IEEE Transactions on Affective Computing.

Zhang, G., & Etemad, A. (2023). Distilling EEG Representations via Capsules for Affective Computing. Pattern Recognition Letters, 171, 99-105. https://doi.org/10.1016/j.patrec.2023.05.011

Zhang, G., Davoodnia, V., & Etemad, A. (2022). Parse: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition. IEEE Transactions on Affective Computing, 13(4), 2185-2200. https://doi.org/10.1109/TAFFC.2022.3210441

Zhang, G., & Etemad, A. (2022, May). Holistic Semi-Supervised Approaches for EEG Representation Learning. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 1241-1245). IEEE. https://doi.org/10.1109/ICASSP43922.2022.9746528

Zhang, G., & Etemad, A. (2022, February). Pairwise Representation Alignment for Semi-Supervised EEG-Based Emotion Recognition. In AAAI Workshop on Human-Centric Self-Supervised Learning.

Zhang, G., & Etemad, S. A. (2021). Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 1138-1149. https://doi.org/10.1109/TNSRE.2021.3089594

Zhang, G., & Etemad, A. (2021, September). Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition. In 9th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 1-8). IEEE. https://doi.org/10.1109/ACII52823.2021.9597449

Zhang, G., Davoodnia, V., Sepas-Moghaddam, A., Zhang, Y., & Etemad, A. (2019). Classification of Hand Movements from EEG Using a Deep Attention-Based LSTM Network. IEEE Sensors Journal, 20(6), 3113-3122. https://doi.org/10.1109/JSEN.2019.2956991

Zhang, G., Morin, E., Zhang, Y., & Etemad, S. A. (2018, July). Non-Invasive Detection of Low-Level Muscle Fatigue Using Surface EMG with Wavelet Decomposition. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5648-5651). IEEE. https://doi.org/10.1109/EMBC.2018.8513588