Abstract
EEG, Electroencephalography, is the acquisition and decoding of electric brain signals. The data acquired from EEG scans can be put to use in many fields, including seizure prediction, treatment of mental illness, brain-computer interfaces (BCIs) and more. Recent advances in deep learning (DL) in fields of image classification and natural language processing have motivated researchers to apply DL for classification of EEG signals as well. One major caveat in DL is the amount of human effort and expertise required for the development of efficient and effective neural network architectures. Neural architecture search algorithms are used to automatically find good enough neural network architectures for a problem and dataset at hand. In this research, we employ genetic algorithms for optimizing neural network architectures for multiple tasks related to EEG processing while addressing two unique challenges related to EEG: (1) small amounts of labeled EEG data per subject, and (2) high diversity of EEG signal patterns across subjects. Neural network architectures produced during this study successfully compete with state of the art architectures published in the literature. Particularly successful are architectures optimized for all (human) subjects, with evolution and training performed on a mixed dataset including all subjects’ data.
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References
Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008, (IEEE World Congress on Computational Intelligence), pp. 2390–2397. IEEE (2008)
Antonenko, P., Paas, F., Grabner, R., Van Gog, T.: Using electroencephalography to measure cognitive load. Educ. Psychol. Rev. 22(4), 425–438 (2010)
Britton, J.W., et al.: Electroencephalography (EEG): an introductory text and atlas of normal and abnormal findings in adults, children, and infants. American Epilepsy Society, Chicago (2016)
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI Competition 2008-Graz Data Set A (2008)
Chambon, S., Galtier, M.N., Arnal, P.J., Wainrib, G., Gramfort, A.: A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans. Neural Syst. Rehabil. Eng. 26(4), 758–769 (2018)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report. Citeseer (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42, 11–24 (2014)
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional network for EEG-based brain-computer interfaces. arXiv preprint arXiv:1611.08024 (2016)
Leeb, R., Brunner, C., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI Competition 2008-Graz Data Set B. Graz University of Technology, Austria (2008)
Lopez, S., Suarez, G., Jungreis, D., Obeid, I., Picone, J.: Automated identification of abnormal adult EEGs. In: 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1–5. IEEE (2015)
Margaux, P., Emmanuel, M., Sébastien, D., Olivier, B., Jérémie, M.: Objective and subjective evaluation of online error correction during p300-based spelling. Adv. Hum.-Comput. Interact. 2012, 4 (2012)
Miikkulainen, R., et al.: Evolving deep neural networks. In: Artificial Intelligence in the Age of Neural Networks and Brain Computing, pp. 293–312. Elsevier (2019)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clin. Neurophysiol. 120(11), 1927–1940 (2009)
Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. In: IJCAI, vol. 89, pp. 762–767 (1989)
NeuroTechX: Neurotechx/moabb, February 2019. https://github.com/NeuroTechX/moabb
Oh, S.L., et al.: A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl. 1–7 (2018)
Ordóñez, F., Roggen, D.: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)
Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. arXiv preprint arXiv:1802.01548 (2018)
Real, E., et al.: Large-scale evolution of image classifiers. arXiv preprint arXiv:1703.01041 (2017)
Roggen, D., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240. IEEE (2010)
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for eeg decoding and visualization. Hum. Brain Mapp. 38(11), 5391–5420 (2017)
Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994)
Tang, Z., Li, C., Sun, S.: Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik-Int. J. Light Electron Opt. 130, 11–18 (2017)
Völker, M., Schirrmeister, R.T., Fiederer, L.D., Burgard, W., Ball, T.: Deep transfer learning for error decoding from non-invasive EEG. In: 2018 6th International Conference on Brain-Computer Interface (BCI), pp. 1–6. IEEE (2018)
Wang, B., Sun, Y., Xue, B., Zhang, M.: Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
Whitley, D., et al.: Genetic algorithms and neural networks. Genetic Algorithms Eng. Comput. Sci. 3, 203–216 (1995)
Zoefel, B., Huster, R.J., Herrmann, C.S.: Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 54(2), 1427–1431 (2011)
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This research was supported by the Israeli Ministry of Defense.
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Rapaport, E., Shriki, O., Puzis, R. (2019). EEGNAS: Neural Architecture Search for Electroencephalography Data Analysis and Decoding. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_1
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