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Seakleng Lim

Seakleng Lim

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Faculty members

Mr. Seakleng Lim

Research Assistant (Intern)

Mr. Seakleng Lim profile

Publications

1. Park, S. M., Yu, X., Chum, P., Lee, W. Y., & Sim, K. B. (2017). Symmetrical feature for interpreting motor imagery EEG signals in the brain-computer interface. Optik, 129, 163–171. https://doi.org/10.1016/j.ijleo.2016.10.047
2. Yu, X., Chum, P., & Sim, K. B. (2014). Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik, 125(3), 1498–1502. https://doi.org/10.1016/j.ijleo.2013.09.013
3. Pharino, C. (2015). A Comparison Study for an Optimal Common Spatial Pattern Algorithm for EEG Signal Classification applicable to BCI Systems. 8TH AUN/SEED-NET INT’L CONFERENCE ON EEE 2014, 1–14.
4. Yu, X., Park, S. M., Ko, K.-E., Chum, P., & Sim, K. (2013). A study on STFT Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 8726.
5. Pharino Chum, Park, S.-M., & Kwang-Eun Ko and Kwee-Bo Sim. (2013). VCSP Method for EEG Feature Extraction of Motor Imagery Brain-Computer Interface. Proceedings of KIIS Spring Conference, 23(1), 115–116.
6. Yu, X., Park, S., Ko, K., Pharino, C., & Sim, K. (2012). Discriminative Power Band Feature Selection using PCA for Motor Imagery Classification in EEG-based BCI System. Proceedings of KIIS Fall Conference, 37–38.

7. Chum, P., Park, S.-M., Ko, K.-E., & Sim, K.-B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 7425 LNCS. https://doi.org/10.1007/978-3-642-32645-5_59
8. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Particle swarm optimization based optimal spatial-spectral-temporal component search in motor imagery brain-computer interface. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7425 LNCS, 469–476. https://doi.org/10.1007/978-3-642-32645-5_59
9. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Improved Method for Extracting Optimal Time-Frequency EEG Signal Feature. Proceedings of KIIS Spring Conference 2012, 22(1), 143–144.
10. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Journal of Korean Institute of Intelligent Systems, 22(6), 793–798.
11. Chum, P., Park, S., Ko, K., & Sim, K. (2012). Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface. Proceedings of KIIS Fall Conference 2012, 22(2), 19–20.

12. Chum, P., Park, S. M., Ko, K. E., & Sim, K. B. (2012). Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement. IECON Proceedings (Industrial Electronics Conference), 1561–1566. https://doi.org/10.110/IECON.2012.6388508
13. Chum, P., Kim, J., Park, S., Ko, K., & Sim, K. (2012). Particle Swarm Optimization based Automatic Feature Selection for Spatio-Spectral- Temporal Filtering in Brain Computer Interface. International Conference on Smart Convergence Technologies and Applications 2012, 2(3), 2012.
14. Chum, P., Park, S.-M. M., & Sim, K. B. (2013). Parallel model feature extraction to improve performance of a BCI system. Journal of Institute of Control, Robotics and Systems, 19(2012), 1022–1028. https://doi.org/10.5302/J.ICROS.2013.13.1930.

Service and Leadership:
• Cambodia Robocon Committee

Contact Details:
• Email: pharino.chum@gmail.com
• Tel: (855) 89-910-904
• Linkedin: www.linkedin.com/in/pharino-chum-446b5a85

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