- Volume 14 Issue 4
DOI QR Code
Improving the Subject Independent Classification of Implicit Intention By Generating Additional Training Data with PCA and ICA
- Oh, Sang-Hoon (Division of Information Communication Convergence Eng. Mokwon University)
- Received : 2018.07.25
- Accepted : 2018.10.24
- Published : 2018.12.28
EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.
- Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, May. 2015, pp. 436-444. https://doi.org/10.1038/nature14539
- A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing 25, 2012, MIT Press, Cambridge, MA.
- B. K. Kim, J. Roh, S. Y. Dong, and S. Y. Lee, "Hierarchical Committee of Deep Convolutional Neural Networks for Robust facial Expression Recognition," J. Multimodal User Interfaces, vol. 10, issue. 2, Jun. 2016, pp. 173-189. https://doi.org/10.1007/s12193-015-0209-0
- J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-Computer Interfaces for Communication and Control," Clin. Neurophysiol, vol. 113, 2002, pp. 767-791. https://doi.org/10.1016/S1388-2457(02)00057-3
- B. Blankertz, G. Dornhege, M. Krauledat, K. R. Muller, V. Kunzmann, F. Losch, and G. Curio, "The Berlin Brain-Computer Interface: EEG-based Communication without Subject Training," IEEE Trans. Neural Sys. and Rehabil., vol. 14, no. 2, 2006, pp. 147-152. https://doi.org/10.1109/TNSRE.2006.875557
- J. H. Lim, H. J. Hwang, C. H. Han, K. Y. Jung, and C. H. Im, "Corrigendum: Classification of Binary Intentions for Individuals with Impaired Oculomotor Function: 'eyes-closed' SSVEP-based Brain-Computer Interface," J. Neural Eng., vol. 10, 2013, pp. 1-9.
- L. A. Farwell and E. Donchin, "The truth Will Out: Interrogative Polygraphy ("Lie Detection") with Event-related brain Potentials," Psychophysiology, vol. 28, 1991, pp. 531-547. https://doi.org/10.1111/j.1469-8986.1991.tb01990.x
- C. Davatzikos, K. Ruparel, Y. Fan, D. G. Shen, M. Acharyya, J. W. Loughead, R. C. Gur, and D. D. Langleben, "Classifying Spatial Patterns of Brain Activity with Machine Learning Methods: Application to Lie Detection," Neuroimage, vol. 28, 2005, pp. 663-668. https://doi.org/10.1016/j.neuroimage.2005.08.009
- S. Y. Dong, B. K. Kim, and S. Y. Lee, "EEG-based Classification of Implicit Intention during Self-relevant Sentence Reading," IEEE Trans. Cybernetics, vol. 46, Nov. 2016, pp. 2535-2542. https://doi.org/10.1109/TCYB.2015.2479240
- S. Y. Dong, B. K. Kim, and S. Y. Lee, "Implicit Agreeing/Disagreeing Intention While Reading Self-relevant Sentences: A Human fMRI Study," Social Neuroscience, vol. 11, issue. 3, 2016, pp. 221-232. https://doi.org/10.1080/17470919.2015.1059362
- S. H. Oh, "Subject Independent Classification of Implicit Intention Based on EEG Signals," Int. Journal of Contents, vol. 12, no. 3, Sep. 2016, pp. 12-16. https://doi.org/10.5392/IJOC.2016.12.3.012
- S. Haykin, Neural Networks, MacMillan, 1994.
- T. W. Lee, M. Girolami, and T. J. Sejnowski, "A Unifying Information Theoretic Framework for Independent Component Analysis," Computers & Mathematics with Applications, vol. 31, no. 11, Mar. 2000, pp. 1-21. https://doi.org/10.1016/0898-1221(96)00057-0
- A. J. Bell and T. J. Sejnowski, "An Information-Maximisation Approach to Blind Separation and Blind Deconvolution," Neural Computation, vol. 7, no. 6, Nov. 1995, pp. 1004-1034.
- S. Amari, "Natural Gradient Works Efficiently in Learning," Neural Computation, vol. 10, Feb. 1998, pp. 251-276. https://doi.org/10.1162/089976698300017746
- C. M. Bishop, Pattern Recognition and Machine Learning, Springer Science+Business Media, LLC, New York, 2006.
- T. W. Lee, M. Girolami, and T. J, Sejnowski, "Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources," Neural Computation, vol. 11, 1999, pp. 417-441. https://doi.org/10.1162/089976699300016719
- C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, Apr. 2011, pp. 1-27. http://www.csie.ntu.edu.tw/-cjlin/libsvm