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

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus

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.
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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.

Mentions: We also ran the Fast Fourier Transform (FFT) power spectral analysis with the EEG data to rule out the possibility that of our findings simply reflect the varying power of alpha waves in the three tested conditions. Alpha waves are in the frequency range of 7.5–12.5 Hz (Berger, 1929) and are known for synchronous, and coherent sinusoidal oscillations in EEG brain signals (Nunez et al., 2001; Gerrard and Malcolm, 2007). Therefore, alpha waves are probably most predictable neural oscillations in EEG brain signals. In our FFT power spectral analysis results (Figure 5), alpha waves were not notably observed for all data, even in the awake states. This is partly because alpha waves are reduced with open eyes, drowsiness, and sleep. Note that in the EEG data we analyzed the participants were conducting normal activity at home with open eyes when the AWAKE EEG data were recorded. Therefore, it seems that there is no strong association between IPI prediction and the alpha wave spectral power in the EEG data.


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

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043151&req=5

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.
Mentions: We also ran the Fast Fourier Transform (FFT) power spectral analysis with the EEG data to rule out the possibility that of our findings simply reflect the varying power of alpha waves in the three tested conditions. Alpha waves are in the frequency range of 7.5–12.5 Hz (Berger, 1929) and are known for synchronous, and coherent sinusoidal oscillations in EEG brain signals (Nunez et al., 2001; Gerrard and Malcolm, 2007). Therefore, alpha waves are probably most predictable neural oscillations in EEG brain signals. In our FFT power spectral analysis results (Figure 5), alpha waves were not notably observed for all data, even in the awake states. This is partly because alpha waves are reduced with open eyes, drowsiness, and sleep. Note that in the EEG data we analyzed the participants were conducting normal activity at home with open eyes when the AWAKE EEG data were recorded. Therefore, it seems that there is no strong association between IPI prediction and the alpha wave spectral power in the EEG data.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus