Proceedings, First IEEE International Symposium on Biomedical Imaging, Washington DC, July 9, 2002

 

 

FAR-FIELD ELECTROPHYSIOLOGY REFLECTS TOP-DOWN CONTROL

 

Scott Makeig

 

Swartz Center for Computational Neuroscience

Institute for Neural Computation, University of California San Diego

smakeig@ucsd.edu

 


Abstract

 

For forty years, much human electrophysiologic thinking has been based on the concept that EEG data recorded from the scalp following sensory stimulation are dominated by successive far-field correlates of bottom-up brain sensory processing, as represented in evoked potential (EP) averages.  I will present evidence for an alternate view that human EEG data are dominated by by oscillatory processes relating to time-varying, top-down control of cortical dynamics and attention. This view suggests a reorientation of scientific and engineering focus towards modeling brain dynamics of humans as active operators rather than as passive perceivers and programmed responders.  Such research presents new engineering challenges. There is a need, first, to understand and model the process of partial phase resetting of ongoing and intermittent nonlinear oscillatory processes, and more generally, of inter-process synchronization. It is probable that real time signal processing capable of separating EEG processes from non-brain signals and of monitoring brain synchronization events may allow high-level cognitive monitoring that could be used in man-machine interfaces and for neuropsychological training.

 

1. Introduction

 

Historically, electroencephalographic (EEG) data analysis has been a technically underdeveloped area of neuroscience. Typically, psychologists using Event-Related Potential (ERP) methods prefer to use simple but limited measures of peak amplitudes and latencies in ERP averages at single scalp channels. Our recent work, together with very recent neurophysiologic evidence from humans and animals, suggests that ERPs averaging data from a number of experimental trials conceal rather than reveal the essential nature of event-related brain dynamics [1]. These results show that active brain responses to significant events or external stimuli involve synchronized oscillations in local field potentials in a number of brain regions [2]. These brain dynamic events appear to begin in the frontal cortex, implying they carry or channel top-down information about intention, including attentional focus, to sensorimotor brain areas [3] triggering other dynamic events that carry or channel bottom-up information from sensory to response-selection areas [4]. Methods we have developed or integrated for EEG research, including Independent Component Analysis (ICA) and time/frequency analysis, now allow detection and modeling of such brain dynamic events from high-dimensional electrical data collected noninvasively from the human scalp.

 

1.1 Limitations of ERP standard methods.

Contrary to the common assumption that event-related EEG data are comprised of an average ERP plus unrelated EEG activity, our recent analyses show that ERP are generated chiefly by imbalances in the distribution of spectral phase in EEG processes whose frequency contents and spatial projections to the scalp vary across individuals. The averaged event-related potential (ERP) evoked by brief unattended visual stimuli, for example, consists of a sequence of positive and negative peaks that are generally assumed to reflect activity in individual visual cortical processing regions. In this view, response averaging removes 'background' electroencephalographic (EEG) activity, whose time course is presumed to be independent of experimental events, as well as artifactual potentials produced by eye and muscle activity, sparing scalp potentials produced by information processing within cortical areas. Other researchers have claimed that some ERP features may also arise from perturbations in the ongoing neural synchrony generating the scalp EEG [5][6].

 

1.2 Visualizing brain dynamics in single trials

Recently, we introduced a new method of visualizing the relation of single-trial EEG dynamics to time locking events: the 'ERP-image' [7][8][9]. ERP-image visualization allows relationships between dependent variables (e.g., reaction time, alpha component phase, gamma band amplitude, etc.) and time-and-phase locked response dynamics to be seen clearly. Combining ICA with ERP-image visualization allows temporally and functionally independent components of EEG dynamics to be separated and visualized. For example, we [1] have recently investigated which model of ERP generation more accurately characterizes dynamic EEG changes occurring during the prominent (N1) negativity in the ERP peaking near 200 ms after sudden onsets of visual stimuli. The results contradict the standard model of event-related brain dynamics that underlies almost all ERP research. This traditional model holds that event-related EEG data are the sum of an averaged ERP plus `background' EEG, based on two assumptions: (1) Each response-generating brain area, silent in the EEG recording prior to the experimental events of interest, becomes briefly and reliably active with a similar time course and polarity following each event, (2) While ongoing EEG activity remains wholly unaffected.

 

While details of the biophysics of the synchronous neural activity that produces the scalp-recorded EEG are not yet well understood, there is no reason to expect that cortical areas contributing to ERPs may not also exhibit synchronized activity before as well as after experimental events, thus contributing to the ongoing EEG, and that EEG processes may not be perturbed by stimuli or cognitive events.  Conclusions from data presented in [1] cast strong doubt on the basic assumption that ERP peaks are produced within just those cortical processing areas that show post-stimulus neural firing rate increases. Instead, they suggest that the visual evoked responses are largely produced by event-related changes in the statistical distribution of phase, relative to the inducing events, of oscillatory activity within spatial domains of neural synchrony that generate the ongoing EEG.

 

1.3. EEG spectral dynamics

In the last decade, a new wave of EEG analysis approaches has emerged in which the object of study is not solely the amplitudes and latencies of peaks in ERP averages, but event-related changes in EEG dynamics in single event-related data epochs. An early effort in this direction was by Pfurtscheller and Araniber [10], who first reported a method for quantifying the average transient suppression of alpha band (circa 10-Hz) activity following stimulation.  In the last decade, researchers studying Pfurtscheller's event-related desynchronization (ERD, spectral amplitude decreases), and event-related synchronization (ERS, spectral amplitude increases) in a variety of narrow frequency bands (4-40 Hz) have reported on their systematic dependencies on task and cognitive state variables as well as on stimulus parameters [11]. For example, Williamson et al. [12] reported that, given a visually presented arithmetic problem to compute mentally, the resulting subject alpha-band ERD resolved only when the calculation was complete. Makeig [13] reported event-related changes in the full EEG spectrum, yielding a 2-D time/frequency measure he called the event-related spectral perturbation (ERSP). This method avoided problems associated with analysis of a priori narrow frequency bands, since bands of interest for the analysis could be based on significant features of the complete time/frequency transform. The ERSP method was adapted under different names by Bertrand and colleagues, and by other European groups studying event-related gamma band (30-100 Hz) EEG phenomena [14]. Its adoption coincided with the new availability to EEG laboratories of scientific workstations with sufficient computational power to complete these analyses quickly.

 

1.4. Event-related coherence

In 1994, Rappelsburger and colleagues introduced event-related coherence (ERCOH) elaborating on creative studies of EEG coherence by Petsche and colleagues [15][16]. The application of spectral coherence to EEG analysis had had a long history, though it had been used infrequently because of its relative computational complexity and difficulty in interpretation. Petsche showed, for example, that in creative men imagining how to draw a picture representing an abstract concept (for example, "liberty"), the distinguishing EEG characteristic was increased coherence between electrodes located over homologous left and right hemispheric locations, whereas the distinguishing characteristic in creative women was exactly the opposite [17]. Another application of coherence to ERP analysis was introduced by the Bertrand group [14] who demonstrated a measure of the consistency of phase consistency at each time surrounding an experimental event class, dubbing this a "phase locking factor." Makeig and colleagues [8] proposed the term "inter-trial coherence" (ITC) for this measure.

 

1.5. Independent Component Analysis

In 1995, Bell and Sejnowski [18] published an iterative algorithm based on information theory for decomposing linearly mixed signals into temporally independent by minimizing their mutual information. The problem of blind separation of recorded multi-channel signals into sums of temporally independent sources had been posed some years earlier. First approaches to blind source separation minimized third and fourth-order correlations among the observed variables and achieved limited success in simulations [19] and approach extended by Cardoso and Laheld [2] in their JADE algorithm. Bell and Sejnowski [21] generalized this approach, demonstrating a simple neural network algorithm that used joint information maximization or 'infomax' as a training criterion. By using a compressive nonlinearity to transform the data and then following the entropy gradient of the resulting mixtures, they were able to demonstrate unmixing of ten recorded voice and music sound sources that had been mixed with different weights in ten simulated microphone channels. Their algorithm used only minimal assumptions about the nature of the sources to be separated. Mixing weights (and thus scalp projections) of individual components were assumed to be fixed, and the time courses of the sources mutually independent. Bell & Sejnowski demonstrated a similar approach for performing blind deconvolution, and later applied their 'infomax' method to decomposition of visual scenes [22].

1.6. Applications to EEG Analysis

The first applications of blind decomposition to biomedical time series analysis were presented by Makeig et al. [23][24], who applied the infomax ICA algorithm to decomposition of EEG and event-related potential (ERP) data and reported the use of ICA to monitor alertness. This first report demonstrated segregation of eye movements from brain EEG phenomena, and separation of EEG data into constituent components defined by spatial stability and temporal independence. Subsequent technical reports [25][26] demonstrated successful separation of six simulated EEG sources mixed into six simulated EEG channels using a realistic three-shell head model. Unmixing performance of the ICA algorithm was shown to degrade gracefully in the presence of noise added to simulate sensor noise or additional small EEG sources. A range of biomedical applications of ICA have recently been reviewed by Jung et al., [27].

 

2. COMBINING ICA WITH TIME-FREQUENCY ANALYSIS

 

In general, oscillatory event-related time/frequency dynamics appear to be more functionally relevant indices of event-related brain dynamics than peaks in the average ERP. However, they are largely invisible to analysis of averaged evoked responses. Our analyses clearly show that independent component brain networks are rapidly linearly coupled and decoupled at a wide range of specific frequency bands in accord with task events and subject behavior. The picture that emerges from these results is that a forced-choice response demand induces coherent theta activity in a distributed network of frontal and parietal cortical regions. Often, the theta activity and coherence replaces pre-existing coherences at higher EEG frequencies. We have seen similar patterns of induced coherent theta bursts following both nontarget and target stimuli in a visual selective attention task, in response to targets stimuli in an auditory response task, and in response to adverse target trajectories in a compensatory tracking task. Thus, we believe that frontal-parietal theta network dynamics, well represented in event-related scalp EEG data, indexes the brain's monitoring of and response to momentary cognitive challenges.

 

6.  APPLICATIONS

 

It appears good measures of performance or capacity to perform can be derived from brain synchronization events supporting cognitive engagement. The same technology that can find nonlinear correlations between physiological measures and moment-to-moment fluctuations in performance in monitoring tasks should also be demonstrable in other task situations and may find applications in cognitive monitoring. 

 

 

11. References

 

[1] Makeig S, Westerfield, M.,  Jung T-P, Townsend, Courchesne, E.  and Sejnowski TJ, Electroencephalographic sources of visual evoked responses . Science 295:690-4, 2002.

 

[2] Klopp, J., Marinkovic, K., Chauvel, P., Nenov, V., and Halgren, E. Early widespread cortical distribution of coherent fusiform face selective activity. Human Brain Mapping, 11, 286-293, 2000.

 

 [3] von Stein, A., Chiang, C., and Konig, P. Top-down processing mediated by interareal synchronization. Proceedings of the National Academy of Sciences USA, 97(26), 14748-14753, 2000.

 

[4] Fries, P., Reynolds, J., Rorie, A., and Desimone, R. Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention. Science, 291(5508), 1560-63, 2001.

 

[5] Basar, E. EEG-brain dynamics : relation between EEG and Brain evoked potentials, Elsevier/North-Holland Biomedical Press, New York, N.Y., 1980.

 

[6] Brandt, M. E. Visual and auditory evoked phase resetting of the alpha EEG. International Journal of Psychophysiology, 26(1-3), 285-98, 1997

 

 [7] Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ. Analyzing and visualizing single-trial event-related potentials, In: Advances in Neural Information Processing Systems, 11:118-24, 1996.

 

[8] Makeig, S., Westerfield, M., Jung, T.-P., Covington, J., Townsend, J., Sejnowski, T. J., and Courchesne, E.  Independent components of the late positive event-related potential in a visual spatial attention task. Journal of Neuroscience, 19(7), 2665-2680, 1999.

 

[9] Jung T-P, Makeig S, Humphries C, Lee T-W, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation, Psychophysiology, 37:163-78, 2001.

 

 [10] Pfurtscheller, G., and Aranibar, A.. Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalography and Clinical Neurophysiology, 42, 817-826.1977.

 

[11] Pfurtscheller, G., and Andrew, C. Event-Related changes of band power and coherence: methodology and interpretation. Journal of Clinical Neurophysiology, 16(6), 512-519, 1999.

 

[12] Williamson, S. J., Kaufman, L., Curtis, S., Lu, Z. L., Michel, C. M., and Wang, J. Z. Neural substrates of working memories are revealed magnetically by the local suppression of alpha rhythm. Electroencephalography and Clinical Neurophysiology. Supplement, 47, 163-80, 1996.

 

[13] Makeig S. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalog. clin. Neurophysiolog., 86:283-93, 1993.

 

[14] Tallon-Baudry, C., Bertrand, O., Delpuech, C., and Pernier, J. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. Journal of Neuroscience, 16(13), 4240-9, 1996.

 

[15] Petsche, H., Kaplan, S., von Stein, A., and Filz, O. The possible meaning of the upper and lower alpha frequency ranges for cognitive and creative tasks. International Journal of Psychophysiology, 26, 77-97, 1997.

 

[16] Sarnthein, J., Petsche, H., Rappelsberger, P., Shaw, G. L., and von Stein, A.Synchronization between prefrontal and posterior association cortex during human working memory. Proceedings of the National Academy of Sciences of the United States of America, 95(12), 7092-6, 1998.

 

[17] Petsche, H. Approaches to verbal, visual and musical creativity by EEG coherence analysis. International Journal of Psychophysiology, 24(1-2), 145-59, 1996.

 

[18] Bell, A. J., and Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-59, 1995.

 

[19] Comon, P. Independent component analysis, a new concept? Signal Processing, 36(3), 287-314, 1994.

 

[20] Cardoso, J. F., and Laheld, B. H. Equivariant adaptive source separation. IEEE Transactions on Signal Processing, 44(12), 3017-30, 1996.

 

[21] Bell, A. J., and Sejnowski, T. J. Learning the higher-order structure of a natural sound, Network: Computation in Neural Systems 7:261-266. 1996.

 

[22] Bell, A., J., and Sejnowski, T. J. The 'Independent components' of natural scenes are edge filters, Vision Research, 37(23) 3327-3338, 1997.

 

[23] Makeig S, Bell AJ, Jung T-P, Sejnowski TJ. Independent Component Analysis of Electroencephalographic Data, In: Advances in Neural Information Processing Systems 8:145-51, 1996.

 

[24] Makeig S, Jung T-P,  Bell AJ, Ghahremani D, Sejnowski TJ. Blind separation of event-related brain responses into independent components, Proc. Natl. Acad. Sci. USA, 94:10979-84, 1997.

 

 [25] Ghahremani D, Makeig S, Jung T-P, Bell AJ, Sejnowski TJ. Independent component analysis of simulated EEG using a three-shell spherical head model, Tech Rep. INC-9601, Institute for Neural Computation, University of California, San Diego, 1996.

 

[26] Makeig S, Jung T-P, Ghahremani D and Sejnowski TJ, Independent Component Analysis of Simulated ERP Data, Tech Rep. INC-9606, Institute for Neural Computation, University of California, San Diego, 1996.

 

[27] Jung T-P, Makeig S, McKeown M.J., Bell, A.J. , Lee T-W, and Sejnowski TJ. Imaging Brain Dynamics Using Independent Component Analysis, Proceedings of the IEEE. 89, 1107-22, 2001.