Abstract
Deep learning, and in particular Deep Belief Network (DBN), has recently witnessed increased attention from researchers as a new classification platform. It has been successfully applied to a number of classification problems, such as image classification, speech recognition and natural language processing. However, deep learning has not been fully explored in electroencephalogram (EEG) classification. We propose in this paper three implementations of DBNs to classify multichannel EEG data based on different channel fusion levels. In order to evaluate the proposed method, we used EEG data that has been recorded to study the modulatory effect of transcranial direct current stimulation. One of the proposed DBNs produced very promising results when compared to three well-established classifiers; which are Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Extreme Learning Machine (ELM).
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References
Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689–696 (2011)
Jacobson, L., Ezra, A., Berger, U., Lavidor, M.: Modulating oscillatory brain activity correlates of behavioral inhibition using transcranial direct current stimulation. Clin. Neurophysiol. 123(5), 979–984 (2012)
Powell, T.Y., Boonstra, T.W., Martin, D.M., Loo, C.K., Breakspear, M.: Modulation of cortical activity by transcranial direct current stimulation in patients with affective disorder. PloS one 9(6), e98503 (2014)
Wulsin, D., Gupta, J., Mani, R., Blanco, J., Litt, B.: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J. Neural Eng. 8(3), 036015 (2011)
Zheng, W.L., Zhu, J.Y., Peng, Y., Lu, B.L.: EEG-based emotion classification using deep belief networks. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2014)
Srivastava, N., Salakhutdinov, R.: Learning representations for multimodal data with deep belief nets. In: International Conference on Machine Learning Workshop (2012)
Cheng, D., Sun, T., Jiang, X., Wang, S.: Unsupervised feature learning using markov deep belief network. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 260–264. IEEE (2013)
Do, V., Xiao, X., Chng, E.: Comparison and combination of multilayer perceptrons and deep belief networks in hybrid automatic speech recognition systems. In: Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (2011)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Wu, Y., Cai, H.: A simulation study of deep belief network combined with the self-organizing mechanism of adaptive resonance theory. In: 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1–4. IEEE (2010)
Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal. Comput. Methods Programs Biomed. 109(3), 339–345 (2013)
Kalu, U., Sexton, C., Loo, C., Ebmeier, K.: Transcranial direct current stimulation in the treatment of major depression: a meta-analysis. Psychol. Med. 42(09), 1791–1800 (2012)
Loo, C.K., Alonzo, A., Martin, D., Mitchell, P.B., Galvez, V., Sachdev, P.: Transcranial direct current stimulation for depression: 3-week, randomised, sham-controlled trial. Br. J. Psychiatry 200(1), 52–59 (2012)
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Al-kaysi, A.M., Al-Ani, A., Boonstra, T.W. (2015). A Multichannel Deep Belief Network for the Classification of EEG Data. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_5
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DOI: https://doi.org/10.1007/978-3-319-26561-2_5
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