Facial Expression Decoding based on fMRI Brain Signal



Brain-computer interface, machine learning, extreme learning machine, fMRI, image processing


The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.


Arlot, S.; Celisse, A.(2010). A survey of cross-validation procedures for model selection, Statistics Survey, 4, 40-79, 2010. https://doi.org/10.1214/09-SS054

Blum, A.; Kalai, A.(1999). Beating the hold-out:bounds for K-fold and progressive crossvalidation, Proceedings of the twelfth annual conference on Computational learning theory. ACM, 203-208, 1999. https://doi.org/10.1145/307400.307439

Davis, M; Whalen, P. J.(2001). The amygdala: vigilance and emotion, Mol Psychiatry, 6(1), 13-34, 2001. https://doi.org/10.1038/sj.mp.4000812

Desikan, R.S.; Segonne, F; Fischl, B.(2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Neuroimage, 31(3), 968-980, 2006. https://doi.org/10.1016/j.neuroimage.2006.01.021

Ekman, P.(1992). An argument for basic emotions, Cognition & Emotion, 6(3-4), 169-200, 1992. https://doi.org/10.1080/02699939208411068

Forman, S D; Cohen, J.D.; Fitzgerald, M. et al.(1992). Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster-Size Threshold, Magnetic Resonance in Medicine, 3(5), 636-647, 2010. https://doi.org/10.1002/mrm.1910330508

Gilead, M.; Liberman, N.; Maril, A.(2013). The language of future-thought: An fMRI study of embodiment and tense processing, Neuroimage, 65(2), 267-279, 2013. https://doi.org/10.1016/j.neuroimage.2012.09.073

Huang, G.B.; Zhu, Q.Y.; Siew, C.K.(2004). Extreme learning machine: a new learning scheme of feedforward neural networks, IEEE International Joint Conference on Neural Networks, 2, 985-990, 2005.

Huang, G. B.; Zhu, Q. Y.; Siew, C.K.(2006). Extreme learning machine: Theory and applications, Neurocomputing, 70(1), 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126

Kearns, M; Ron, D.(1997). Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, Neural Computation, 11(6), 1427-1453, 1997. https://doi.org/10.1162/089976699300016304

Logothetis, N.K.; Pauls, J.; Augath, M.(2001). Neurophysiological investigation of the basis of the fMRI signal, Nature, 412(6843), 150-157, 2001. https://doi.org/10.1038/35084005

Lehmann, T.M; Gonner, C.; Spitzer, K.(1999). Survey: interpolation methods in medical image processing, IEEE Transactions on Medical Imaging, 18(11), 1049-1075, 1999. https://doi.org/10.1109/42.816070

Litvak, V.; Mattout, J.; Kiebel, S. et al. (2011). EEG and MEG Data Analysis in SPM8, Computational Intelligence and Neuroscience, 2011(3), 852961, 2011. https://doi.org/10.1155/2011/852961

Michel, C.M; Murray, M.M.; Lantz, G.(2004). EEG source imaging, Clinical Neurophysiology, 115(10), 2195-2222, 2004. https://doi.org/10.1016/j.clinph.2004.06.001

Rodriguez, J.D.; Perez, A.; Lozano, J.A.(2010). Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation, IEEE Computer Society, 32(3), 569-575, 2010. https://doi.org/10.1109/TPAMI.2009.187

Schalk, G.; Mcfarland, D.J.; Hinterberger, T.(2004). BCI2000: a general-purpose braincomputer interface (BCI) system, IEEE Transactions on Bio-medical Engineering, 51(6), 1034-1043, 2004. https://doi.org/10.1109/TBME.2004.827072

Suykens, J.A.K.(1999). Least squares support vector machine classifiers: a large scale algorithm[ C]. European Conference on Circuit Theory and Design, 1999, 839-842, 1999.

Tugui, A. (2014). GLM Analysis for fMRI using Connex Array International Journal of Computers Communications & Control, 9(6), 768-775, 2014. https://doi.org/10.15837/ijccc.2014.6.1482

Ziegel, E.R.(2012). An Introduction to Generalized Linear Models[J]. Technometrics, 44(4), 406-407, 2012. https://doi.org/10.1198/tech.2002.s91

Zilles, K; Amunts, K.(2010). Centenary of Brodmann's map-conception and fate, Nature Reviews Neuroscience, 11(2), 139-145, 2010. https://doi.org/10.1038/nrn2776



Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.