Modeling and Tracking Brain Nonstationarity in a Sustained Attention Task

  • Sheng-Hsiou HsuEmail author
  • Tzyy-Ping Jung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


In real-life situations, where humans optimize their behaviors to effectively interact with unknown and dynamic environments, their brain activities are inevitably nonstationary. Electroencephalogram (EEG), a widely used neuroimaging modality, has a high temporal resolution for characterizing the brain nonstationarity. However, quantitative measurements of EEG nonstationarity and its relations with human cognitive states and behaviors are still elusive. This study hypothesized that EEG nonstationarity could be modeled as changes of active sources decomposed by an Independent Component Analysis and proposed a model-based nonstationarity index (NSI) to quantitatively assess these changes. We tested the hypothesis and evaluated the NSI on EEG data collected from eight subjects performing a sustained attention task. Empirical results showed that values of the proposed NSI were significantly different when the subjects exhibited different levels of behavioral performance that inferred their brain states. The proposed approach is online-capable and can be used to track EEG nonstationarity in near real-time, which enables applications such as monitoring brain states during a cognitive task or predicting human behaviors in a brain-computer interface.


Independent Component Analysis (ICA) EEG Nonstationarity 


  1. 1.
    Jung, T.P., Makeig, S., Stensmo, M., Sejnowski, T.J.: Estimating alertness from the EEG power spectrum. IEEE Trans. Biomed. Eng. 44(1), 60–69 (1997)CrossRefGoogle Scholar
  2. 2.
    Lin, C.T., Huang, K.C., Chao, C.F., Chen, J.A., Chiu, T.W., Ko, L.W., Jung, T.P.: Tonic and phasic EEG and behavioral changes induced by arousing feedback. Neuroimage 52(2), 633–642 (2010)CrossRefGoogle Scholar
  3. 3.
    Hsu, S.-H., Mullen, T., Jung, T.-P., Cauwenberghs, G.: Real-time adaptive EEG source separation using online recursive independent component analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 24(3), 309–319 (2015)CrossRefGoogle Scholar
  4. 4.
    Hsu, S.H., Pion-Tonachini, L., Jung, T.P., Cauwenberghs, G.: Tracking non-stationary EEG sources using adaptive online recursive independent component analysis. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, November 2015, pp. 4106–4109, August 2015Google Scholar
  5. 5.
    Huang, R.-S., Jung, T.-P., Makeig, S.: Event-related brain dynamics in continuous sustained-attention tasks. In: Schmorrow, D.D., Reeves, L.M. (eds.) HCII 2007 and FAC 2007. LNCS (LNAI), vol. 4565, pp. 65–74. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  7. 7.
    Lee, T.-W., Girolami, M., Sejnowski, T.J.: Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11(2), 417–441 (1999)CrossRefGoogle Scholar
  8. 8.
    Chuang, C.H., Ko, L.W., Lin, Y.P., Jung, T.P., Lin, C.T.: Independent component ensemble of EEG for brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 22(2), 230–238 (2014)CrossRefGoogle Scholar
  9. 9.
    Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., Robbins, K.A.: The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 16 (2015)CrossRefGoogle Scholar
  10. 10.
    Mullen, T., Kothe, C., Chi, Y.M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., Jung, T.P.: Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2184–2187, January 2013Google Scholar
  11. 11.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Swartz Center for Computational Neuroscience, Institute for Neural ComputationUniversity of CaliforniaSan DiegoUSA

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