Predictable internal brain dynamics in EEG and its relation to conscious states

Abstract

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

Keywords: EEG; consciousness; neuroevolution; predictable dynamics; sleep.

Figure 1

EEG Data. EEG data (Kemp et al., 2000) from the PhysioBank (Goldberger et al., 2000) are shown. Each row represents data from each subject (four total) and each column represents different states. (A) Awake, raw data. (B) Awake, smoothed (Gaussian filter, σ = 1), and peaks identified (circles). (C) REM, raw data. (D) REM, smoothed and peaks identified. (E) SWS, raw data. (F) SWS, smoothed and peaks identified. Each data set had 30,000 data points but here we are showing only the first 1000 for a better view of the details.

Figure 2

A neural network predictor for time series data. A multi-layer neural network consisting of k = 10 input units, 10 hidden units, and one output unit was trained. The input values were taken from k consecutive values from a time series leading up to time t (time step t − k + 1 to t), and the target output value set to the value at time step t + 1. The network is activated in a feed-forward manner, through the connections, and the error in the output vs. the target value back propagated to adjust the connection weights. See the text for more details.

Figure 3

Summary of EEG IPI prediction error results (mean and standard deviation). Mean and standard deviation of IPI prediction error are shown for all four subjects, for all three conditions (awake, REM, and SWS). The unit for the y-axis was 10 ms. For all subjects, awake and REM conditions resulted in lower IPI prediction error than SWS, showing that predictive dynamics may be more prominent during conscious states. All differences were significant (t-test, p < 10⁻⁶), except for REM vs. AWAKE for subject 4. See text for details. Awake state having higher IPI prediction error than REM state is somewhat unexpected, which we will discuss further in the Discussion section. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.

Figure 4

EEG IPI prediction error distribution. The IPI prediction error distribution is shown for all four subjects, each for all three conditions (awake [red], REM [blue], and SWS [green]). The x-axis is in linear scale while the y-axis is in log scale for a clearer view of the probability of extreme error values. The unit for the x-axis was 10 ms. The trends are consistent for all four subjects. REM has the highest peak near zero error, closely followed by awake state, and finally SWS which shows the lowest peak. SWS has the heaviest tail, meaning that high error values are much more common than awake state or REM. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.

Figure 5

FFT power spectrum of the raw EEG data. FFT power spectrum of each EEG data set is shown. Most peaks are observed near 1 Hz and 2 Hz. Note that the results shown here are based on the raw EEG data, not the IPI data, and that the y-axis are scaled differently to fit the data.

Figure 6

EEG IPI distribution. (A–D) The IPI distributions are shown for all four subjects, for all three conditions (awake [red], REM [blue], and SWS [green]). For all cases, the IPI distributions are positively skewed. The skewness varied from 0.83 to 2.71. The x-axis represents time (unit = 10 ms) and the y-axis frequency. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.