Publications

Working Papers

Choo, S., Park, H., Jung, J., Flores, K., and Nam, C. S. (under revision). EEG data augmentation to improve classification performance of motor imagery BCI using conditional generative adversarial networks.
Ive, J., Santel, D., Glauser, T., Cheng, T., Agasthya, G., Tschida, J., Choo, S., Chandrashekar, M., Kapadia, A., and Pestian, J. (under revision). Mitigating diversity, equity, and inclusion bias in pediatric mental health text.
Choo, S., and Nam C. S. (to be submitted). Effects of batch size and its strategy on generalizability of convolutional neural networks on motor imagery BCI.
Choo, S., Lee, E., Maguire, D., Shivanna, A., Santel, D., Goether, I., Hanson, H., Shekar, M., Kapadia, A., Agasthya, G., Pestian, J., and Glauser, T. (in preparation). Comparing Machine Learning and Deep Learning Models for Pediatric Anxiety Classification using Temporal, Structured, and Environmental Data from Electronic Health Records.
Choo, S., ... (in preparation). Differentially private federated learning with vocab masking for classifying cancer pathology reports.

Peer-reviewed Journal Articles

Park, D., Park, H., Kim, S., Choo, S., Nam, C. S., Lee, S., and Jung, J. (2023). Spatio-temporal explanation of 3D-EEGNet for motor imagery EEG classification using permutation and saliency. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4504-4513.
Choo, S., Park, H., Kim, S., Park, D., Jung, J. Y., Lee, S., and Nam, C. S. (2023). Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition. Expert Systems with Applications, 227, 120348.
Choo, S., and Kim, W. (2023). A study on the evaluation of tokenizer performance in natural language processing. Applied Artificial Intelligence, 37(1), 2175112.
Kim, S., Choo, S., Park, D., Park, H., Nam, C. S., Jung, J. Y., and Lee, S. (2023). Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context. International Journal of Human-Computer Studies, 174, 103009.
Choo, S., and Nam C. S. (2022). Detecting human trust calibration in automation: a convolutional neural network approach. IEEE Transactions on Human-Machine Systems, 52(4), 774-783.
Pugh, Z., Choo, S., Leshin, J., Lindquist, K., and Nam, C. S. (2022). Emotion depends on context, culture, and their interaction: Evidence from effective connectivity in the brain’s fronto-parietal network. Social Cognitive and Affective Neuroscience, 17(2), 206-217.
Huang, J., Choo, S., Pugh, Z. H., and Nam, C. S. (2022). Evaluating effective connectivity of trust in human–automation interaction: A dynamic causal modeling (DCM) study. Human Factors, 64(6), 1051-1069.
Nam, C. S., Choo, S., Huang, J., and Park, J. (2020). Brain-to-brain neural synchrony during social interactions: A systematic review on hyperscanning studies. Applied Sciences, 10(19), 6669.
Kim, W., Jin, B., Choo, S., Nam, C. S., and Yun, M. H. (2019). Designing of smart chair for monitoring of sitting posture using convolutional neural networks. Data Technologies and Applications, 53(2), 142-155.
Choo, S., and Lee, H. (2018). Learning framework of multimodal Gaussian–Bernoulli RBM handling real-value input data. Neurocomputing, 275, 1813–1822.
Choo, S., and Lee, H. (2016). Learning and propagation framework of Bayesian network using metaheuristics and EM algorithm considering dynamic environments. Journal of Korean Institute of Intelligent Systems, 26(6), 335-342.

Conference Proceedings & Posters

Choo, S., Shivanna, A., Goether, I., Santel, D., Pestian, J., Glauser, T., and Agasthya, G. (2023). Machine learning models for predicting pediatric anxiety from structured electronic health records. Artificial Intelligence Expo, Oak Ridge National Laboratory.
Park, H., Park, D., Kim, S., Choo, S., Nam, C. S., Lee, S., Jung, J. (2023). Explaining convolutional neural networks for EEG-based brain-computer interface using influence functions. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4436-4440.
Huang, J., Traylor, Z., Choo, S., and Nam, C. S. (2021). Neural correlates of mental workload during multitasking: A dynamic causal modeling study. In Proceedings of the Human Factors and Ergonomics Society’s 65th Annual Meeting. Sage CA: Los Angeles, CA: SAGE Publications.
Choo, S., Ghasemi, Y., Jeong, H., and Nam, C. S. (2021). Effects of multi-task learning for deep learning on prediction performance of EEG-based cognitive state recognition. In Proceedings of IIE Annual Conference, 334-339.
Choo, S., and Nam, C. S. (2020). Emotion recognition with a CNN using functional connectivity-based EEG features. In Proceedings of the Human Factors and Ergonomics Society’s 64th Annual Meeting. Sage CA: Los Angeles, CA: SAGE Publications.
Choo, S., and Nam, C. S. (2020). DCGAN based EEG data augmentation in cognitive state recognition. In Proceedings of IIE Annual Conference, 1-6.
Choo, S., Sanders, N., Kim, N., Kim, W., and Nam, C. S. (2019). Detecting human trust calibration in automation: A deep learning approach. In Proceedings of the Human Factors and Ergonomics Society’s 63rd Annual Meeting (Vol. 63, No. 1, pp. 88-90). Sage CA: Los Angeles, CA: SAGE Publications.
Sanders, N., Choo, S., Kim, N., and Nam, C. S. (2019). Neural correlates of trust During an Automated System Monitoring Task: Preliminary Results of An EEG Effective Connectivity Study. In Proceedings of the Human Factors and Ergonomics Society’s 63rd Annual Meeting (Vol. 63, No. 1, pp. 83-87). Sage CA: Los Angeles, CA: SAGE Publications.

Book Chapters

Choo, S., and Nam, C. S. (2022). Interactive reinforcement learning and error-related potential classification for implicit feedback. In Nam, C. S., Jung, J., and Lee, S. (Eds). Human-Centered Artificial Intelligence: Research and Applications (pp. 127-143). Elsevier.
Choo, S., and Nam, C.S. (2020). Deep learning techniques in neuroergonomics. Neuroergonomics: Principles and Practices (pp. 115-138). Springer.
Sanders, N., Choo, S., and Nam, C. S. (2020). The EEG cookbook: A practical guide to neuroergonomics research. Neuroergonomics: Principles and Practices (pp. 33-52). Springer.
Nam, C. S., Eskander, E., and Choo, S. (2020). Neural dynamics of trust in human-robot interaction. In Nam, C. S., and Lyons, J. (Eds.). Trust in Human-Robot Interaction: Research and Applications (pp. 477-489). Elsevier.