3. Multi-scale Brain Dynamics in Sustained-attention Tasks


For the past fifty years, research in human electrophysiology has been dominated by event-related potentials (ERPs) that index mean EEG activities following onsets of discrete sensory stimuli. In many ERP paradigms, participants respond to some such stimulus events with single, discrete button presses. In real-life, on the other hand, we receive continuous visual and/or auditory stimulation requiring continuous cognitive effort (e.g., anticipating, perceiving, reasoning, evaluating) to maintain active cognition, i.e. appropriately conditioned complex behavior – not only discrete choice responses with simple motor consequences (like button presses). The brain dynamics associated with this active cognition may vary across multiple scales, both in space and in time.

Recently, colleagues and I reported that during a continuous tracking task without impulsive stimulus onsets, sub-second scale EEG dynamics related to visuomotor task performance could be dissociated from slower spectral modulations linked to changes in performance and arousal (Huang et al, 2008). During a continuous sustained attention task with embedded discrete events, multiple brain areas exhibited sub-second scale phasic and transient EEG dynamics time-locked to stimulus onsets or subject responses. These phasic and/or transient event-related potentials or spectral perturbations might link to attention, which could be dissociated from slower spectral modulations linked to changes in cognitive states such as drowsiness level, motion/scene sickness, etc. (Huang et al, in preparation; Lin et al., 2008; Lin et al. 2010). Ongoing work at SCCN by Huang, Jung, Makeig, and colleagues aims to further develop applications of joint time-frequency ICA analysis to EEG/fMRI analysis to further explore human brain dynamics on multiple temporal scales during a realistic simulated driving task.

Multi-Scale Brain Dynamics

Figure above shows brain dynamics in a sustained-attention visual tracking task (Huang et al., 2008). The task required subjects to use a trackball to keep a drifting (‘wind-blown’) disc as near as possible to a bulls-eye (target ring) at the center of the screen, by making frequent (~3/s) movements of the trackball in the direction of intended movement. Subjects were instructed to continue to perform the task as best as they could even if they began to feel drowsy. Subjects often experienced waves of drowsiness in these 1-hour continuous visuomotor compensatory tracking tasks. The left panels show event-related spectral perturbations (ERSP) under drowsy and alert conditions. The middle panel shows the scalp map of an independent component and its equivalent dipole locations, plus tonic and phasic power spectra. Thin black and magenta curves: mean spectral power baselines preceding disc perigees in low-error and high-error epochs, respectively. Thick blue and red curves: maximum ERSP power in the 0–2.5 s following perigees. Yellow and cyan fills: frequency ranges exhibiting significant (p<0.01) phasic post-perigee power increases in high-error and low-error epochs, respectively. (Black horizontal line segments) Frequencies exhibiting significant (p<0.01) tonic spectral power increases (high-error minus low-error). The right panel shows equivalent-dipole IC source locations and their projections onto average brain images. More details can be found in Huang et al. (2008).

Relevant Publications


  1. •Chuang, S-W., Ko, L-W., Lin, Y-P, Huang, R-S., Jung, T-P., Lin, C-T., Co-modulatory Spectral Changes in Independent Brain Processes Are Correlated with Task Performance, NeuroImage, 62: 1469-77, 2012.

  2. •Chi, Y. M., Wang, Y-T, Wang, Y., Jung, T-P, Cauwenberghs, G. Dry and Non-contact EEG Sensors for Mobile Brain-Computer Interfaces, IEEE Trans Neural System and Rehabilitation Engineering, 20(2): 228-35, 2012.

  3. •Gramann, K., Gwin, J. T., Ferris, D. P., Oie, K., Jung, T-P., Lin, C-T., Liao, L-D, Makeig, S. Cognition in Action: Imaging Brain/Body Dynamics in Mobile Humans, Reviews in the Neurosciences 22(6), 593-608, 2011.

  4. •Wang, Y-T., Wang, Y., Jung, T-P.,  A Cell-Phone Based Brain-Computer Interface for Communication in Daily Life, Journal of Neural Engineering, 8(2), 2011.  [This article was selected as the journal’s Highlights collection for 2011].

  5. •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: 633–42, 2010.

  6. •Chen Y-C, Duann J-R, Chuang S-W, Lin C-L, Ko, L-W, Jung T-P, Lin, C-T. Spatial and Temporal EEG Dynamics of Motion-sickness, NeuroImage, 49:2862-70, 2010.

  7. •Makeig, S., Gramann, K., Jung, T-P., Sejnowski, T.J., Poizner, H. Linking Brain, Mind and Behavior, International Journal of Psychophysiology, 73: 95-100, 2009.

  8. •Ko, L-W., Tsai, I-L., Yang, F-S., Chung, J-F., Lu, S-W., Jung, T-P., and Lin, C-T., "Real-Time Embedded EEG-Based Brain-Computer Interface," Lecture Notes in Computer Science, Advances in Neuro-Information Processing, 1038-45, 2009.

  9. •Huang, R-S., Jung, T-P., Makeig, S. "Tonic Changes in EEG Power Spectra during Simulated Driving," Lecture Notes in Computer Science, Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, 394-403, 2009.

  10. •Jung, T-P. and Makeig, S. "Techniques of EEG Recording and Preprocessing: Independent Component Analysis of Electroencephalographic Data," In: Quantitative EEG Analysis Methods and the Applications (S. Tong & N. Thakor ed.), 39-49, 2009.

  11. •Pal, N.R., Chuang, C-Y., Ko, L-W, Chao, C-F., Jung, T-P., Liang, S-F., Lin, C-T., "EEG-based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach," EURASIP Journal on Applied Signal Processing, 2008.

  12. •Lin, C-T., Ko, L-W, Chiou, J-C, Duann, J-R., Chiu, T-W., Huang, R-S., Liang, S-F, Jung, T-P., A noninvasive prosthetic platform using mobile & wireless EEG, Proceedings of the IEEE, 96(7):1167-83, 2008.

  13. •Huang, R-S, Jung, T-P., Delorme A, Makeig, S. Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking , NeuroImaging, 39:1896-1909, 2008.

  14. •Lin, C-T., Ko, L-W., Chung, I-F., Huang, T-Y., Chen, Y-C., Jung, T-P., and Liang, S-F, Adaptive EEG-based Alertness Estimation System by Using ICA-based Fuzzy Neural Networks,, IEEE Transactions on Circuits and Systems I, 53(11): 2469-76, 2006.

  15. •Lin, C-T., Wu, R-C, Liang, S-F, and Huang, T-Y. Chao, W-H. Chen, Y-J., Jung, T-P., EEG-based Drowsiness Estimation for Safety Driving Using Independent Component Analysis,, IEEE Transactions on Circuit and System, 52(12):2726-38, 2005.

  16. •Lin, C-T., Wu, R-C, Jung, T-P., Liang, S-F, and Huang, T-Y. Estimating Driving Performance Based on EEG Spectrum Analysis , EURASIP Journal on Applied Signal Processing 19: 3165-74, 2005.

  17. •Liang, S.F., Lin, C-T, Wu, R.C, Chen, Y-C, Huang, T.Y. and Jung, T-P, Monitoring Driver's Alertness based on the Driving Performance Estimation and the EEG Power Spectrum , Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.

  18. •Huang, R-S. Jung, T-P and Makeig, S. Analyzing Event-Related Brain Dynamics in Continuous Compensatory Tracking Tasks , Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.

  19. •Makeig S, Jung T-P, and Sejnowski TJ, " Awareness during drowsiness: Dynamics and electrophysiological correlates", Can J Exp Psychol., 54(4): 266-73, 2000.

  20. •Makeig S, Enghoff S, Jung T-P, and Sejnowski TJ, "A Natural Basis for Efficient Brain-Actuated Control", IEEE Trans Rehab Eng, 8:208-11. 2000.

  21. •Jung, T-P, Makeig, S, Stensmo, M, and Sejnowski, TJ, "Estimating alertness from the EEG power spectrum," IEEE Trans Biomed Eng, 44(1), 60-69, 1997.

  22. •Makeig, S and Jung, T-P, "Tonic, phasic and transient EEG correlates of auditory awareness in drowsiness," Cogn Brain Res 4, 15-25, 1996.

  23. •Makeig, S, Jung, T-P, and Sejnowski, TJ, "Using feedforward neural network to monitor alertness from changes in EEG correlation and coherence," Advances in Neural Information Processing Systems 8, 931-937, 1996.