Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning
It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite.
deep learning, Wearables, PHM training, activity recognition, EEG, sensor elimination, multi-modal sensor fusion
Alomari, M. H., Samaha, A., & AlKamha, K. (2013). Automated classification of l/r hand movement eeg signals using advanced feature extraction and machine learning. arXiv preprint arXiv:1312.2877.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8), 1798–1828.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, New York, NY, USA.
Chambayil, B., Singla, R., & Jha, R. (2010). Eeg eye blink classification using neural network. In Proceedings of the world congress on engineering (Vol. 1, pp. 2–5).
Coates, A., Ng, A. Y., & Lee, H. (2011). An analysis of single-layer networks in unsupervised feature learning. In International conference on artificial intelligence and statistics (pp. 215–223).
Delorme, A., & Makeig, S. (2004). Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9–21.
Deng, L., & Dong, Y. (2014). Foundations and trends R in signal processing. Signal Processing, 7, 3–4.
Giering, M., Reddy, K., & Venugopalan, V. (2014). Multi-modal sensor registration for vehicle perception via deep neural networks. Retrieved from
Gu¨ler, N. F., U¨ beyli, E. D., & Gu¨ler, I˙. (2005). Recurrent neural networks employing lyapunov exponents for eeg signals classification. Expert Systems with Applications, 29(3), 506–514.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
Hoogenboom, N., Schoffelen, J.-M., Oostenveld, R., Parkes, L. M.,&Fries, P. (2006). Localizing human visual gammaband activity in frequency, time and space. Neuroimage, 29(3), 764–773.
Iscan, Z., Dokur, Z., & Demiralp, T. (2011). Classification of electroencephalogram signals with combined time and frequency features. Expert Systems with Applications, 38(8), 10499–10505.
Jasper, H. H. (1958). The ten twenty electrode system of the international federation. Electroencephalography and Clinical Neurophysiology, 10, 371–375. Retrieved from http://ci.nii.ac.jp/naid/10020218106/
Kavukcuoglu, K., Sermanet, Y. L., P. Boureau, Gregor, K., Mathieu, M., & LeCun, Y. (2010). Learning convolutional feature hierachies for visual recognition. In Nips.
Kisley, M. A., & Cornwell, Z. M. (2006). Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation. Clinical Neurophysiology, 117(11), 2549–2563.
Kottaimalai, R., Rajasekaran, M. P., Selvam, V., & Kannapiran, B. (2013). Eeg signal classification using principal component analysis with neural network in brain computer interface applications. In Emerging trends in computing, communication and nanotechnology (ice-ccn), 2013 international conference on (pp. 227–231).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Nips.
Lachaux, J.-P., Rodriguez, E., Martinerie, J., Varela, F. J., et al. (1999). Measuring phase synchrony in brain signals. Human brain mapping, 8(4), 194–208.
Larochelle, H., & Bengio, Y. (2008). Classification using discriminative restricted boltzmann machines. In Proceedings of the 25th international conference on machine learning (pp. 536–543).
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Le Van Quyen, M., Foucher, J., Lachaux, J.-P., Rodriguez, E., Lutz, A., Martinerie, J., & Varela, F. J. (2001). Comparison of hilbert transform and wavelet methods for the analysis of neuronal synchrony. Journal of neuroscience methods, 111(2), 83–98.
Morimoto, T., & Sketch, S. (n.d.). Classifying the brain’s motor activity via deep learning.
Naderi, M. A., & Mahdavi-Nasab, H. (2010). Analysis and classification of eeg signals using spectral analysis and recurrent neural networks. In Biomedical engineering (icbme), 2010 17th iranian conference of (pp. 1–4).
Niedermeyer, E., & da Silva, F. H. L. (2005). Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.
Omerhodzic, I., Avdakovic, S., Nuhanovic, A., & Dizdarevic, K. (2013). Energy distribution of eeg signals: Eeg signal wavelet-neural network classifier. arXiv preprintarXiv:1307.7897.
Patnaik, L. M., & Manyam, O. K. (2008). Epileptic eeg detection using neural networks and post-classification. Computer methods and programs in biomedicine, 91(2), 100–109.
Peters, B. O., Pfurtscheller, G., & Flyvbjerg, H. (1998). Mining multi-channel eeg for its information content: an annbased method for a brain–computer interface. Neural Networks, 11(7), 1429–1433.
Roach, B. J., & Mathalon, D. H. (2008). Event-related eeg time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophrenia bulletin, 34(5), 907–926.
Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F. J. (1999). Perception’s shadow: long-distance synchronization of human brain activity. Nature, 397(6718), 430–433.
Sarkar, S., Venugopalan, V., Reddy, K., Giering, M., Ryde, J., & Jaitly, N. (2014). Occlusion edge detection in rgb-d frames using deep convolutional networks. Retrieved from http://arxiv.org/abs/1412.7007
Spencer, K. M., Nestor, P. G., Perlmutter, R., Niznikiewicz, M. A., Klump, M. C., Frumin, M., . . . McCarley, R. W. (2004). Neural synchrony indexes disordered perception and cognition in schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 101(49), 17288–17293.
Subasi, A., Alkan, A., Koklukaya, E., & Kiymik, M. K. (2005). Wavelet neural network classification of eeg signals by using ar model with mle preprocessing. Neural Networks, 18(7), 985–997.
Tsoi, A., So, D., & Sergejew, A. (1993). Classification of electroencephalogram using neural network. In Neural information processing system (Vol. 6, pp. 1–7).
Turner, J., Page, A., Mohsenin, T., & Oates, T. (2014). Deep belief networks used on high resolution multichannel electroencephalography data for seizure detection. In 2014 aaai spring symposium series.
Vartak, A. A., Fidopiastis, C. M., Nicholson, D. M., Mikhael, W. B.,&Schmorrow, D. (2008). Cognitive state estimation for adaptive learning systems using wearable physiological sensors. In Biosignals (2) (pp. 147–152).