Last modified: August 26, 2021
Complexity, TAG Sync and Neurofeedback:
I chose a simple snowflake for the complexity icon because underneath the seemingly infinite
variety of designs there is a simple law of physics that governs the development of complexity
in all inanimate, animate, and technical designs - Bejan’s Constructal Law of Physics (also see
section on CLaw).
“For a finite-size flow system to persist in time (to live), its configuration must evolve in such a
way that provides easier access to the currents that flow through it.” (Bejan A 2012 - Design in
Nature [1]). The snowflake is a living morphing design to disperse heat to the atmosphere
during freezing. The complexity is a result.
The mind, scientific organizations, communications grids, etc., are all living morphing designs to increase the flow of
information through them. Similarly the evolving diversity and complexity in geological formations, plants and animals
provides more efficient flow of matter, energy and information as well.
There is a growing variety of mathematical methods for calculating complexity such as dimensional complexity,
Lyapunov exponent, approximate entropy, Lempel-Ziv complexity, correlation dimension, Hurst exponent, multiscale
entropy, synchrony coalition entropy and others. These are applied to analysis of behavior as well as to biological
signals such as ECG and EEG. This mathematical complexity is not needed to understand the basic concept that
wellness behavior involves evolution of complexity, and sickness behavior involves loss of complexity. Standard
measures of entropy only measure randomness, but biological complexity grows in a special region between rigidity
and randomness such as in small world networks.
Carhart-Harris et al [2] find that normal waking states have intermediate entropy and are critically located between
low entropy states such anesthesia, deep sleep and coma and high entropy states such as REM sleep and
psychedelic states. "...there is a greater repertoire of connectivity motifs in the psychedelic state than in normal
waking consciousness... entropy suppression furnishes normal waking consciousness with a constrained quality and
associated metacognitive functions, including reality-testing and self-awareness." [2]
Yang et al 2013 state [3], “We propose that mental illness is loss of brain complexity and the complexity of mental
illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic
human behavior and microscopic neuronal activity” [2].
Schartner et al (2015) state [4], “Emerging neural theories of consciousness suggest a correlation between a specific
type of neural dynamical complexity and the level of consciousness.” They introduce the novel complexity measure
“synchrony coalition entropy (SCE)” based on “diversity in synchrony patterns”. SCE is low for small phase lag
and also low for large phase lag, but SCE is high for intermediate values “in which synchrony between communities
is able to fluctuate.” The authors point out that “changes in complexity do not simply reflect changes to the overall
power spectrum.”
TAG Sync based neurofeedback is also called
Live Complexity Training (LCT)
in which increased entropy is harnessed by
global broad band synchronization (GBBS) over
small world networks (SWN) operating at or near
self-organized criticality (SOC) according to the
constructal law of physics (CLaw): GBBS / SWN +
SOC & CLaw
Complexity, Vigilance and the EEG:
Gerald Ulrich 2013 [5] describes the appearance and dynamics of the EEG when it represents efficient and adaptive
processing of internal and external information. He calls this state vigilance as had Bente and other Europeans before
him. I call this state adaptive complexity. Ulrich points out that the EEG can not be used to diagnosis diseases, but it is
a global cerebral indicator whose “ipsative” change over time indicates changes between sickness behavior and
wellness behavior. Vigilance behavior and EEG indicate full awake adaptive processing of information and when
disturbed by stress, disease or altered states can exhibit dynamic rigidity and/or dynamic lability. Later we will discuss
the appearance of these changes on the chronospectrogram (cascading spectral display) during neurofeedback.
A summary, in very simple terms, is that the constructal law of physics suggests the role of complexity in adaptive
evolution. Weakness in this system can lead to sickness behavior and inflammatory white matter damage to small
world networks accompanied by intrusions of sleep fragments and other signs of sickness behavior in the EEG.
Complexity and Selected Clinical Considerations:
Acupuncture
Aging
Alzheimer’s Disease & Dementia
Anesthesia
Attention
Autism
Cerebral Palsy
Chronic Fatigue Syndrome
Cognition, Mild Impairment
Coma, Pediatric
Consciousness
Creativity
Depression
Down Syndrome
EEG Recording
Executive Functioning
Epilepsy
Hypoglycemia
Mental Illness
Obsessive Compulsive Disorder
Parkinson’s Disease
Pediatrics
Posttraumatic Stress Disorder
Schizophrenia
Sleep
Sleepiness
Stroke
Acupuncture: ▲
“…a new wavelet limited penetrable visibility graph (WLPVG) approach. Manual acupuncture can influence the
complexity of EEG sub-bands in different ways and lead the functional brain networks to obtain higher efficiency and
stronger small-world property compared with pre-acupuncture control state.”
Pei X, et al (2014) - WLPVG approach to the analysis of EEG-based functional brain network under
manual acupuncture. Cogn Neurodyn. 2014 Oct; 8(5):417-28. [Abstract]
“By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is
lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no
obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min.”
Yi G, et al (2013) - Multi-scale order recurrence quantification analysis of EEG signals evoked by manual
acupuncture in healthy subjects. Cogn Neurodyn. 2013 Feb; 7(1):79-88. [Abstract]
Aging: ▲
“Considering the general "loss of complexity" theory of aging, our finding of increased EEG complexity in elderly people
with heightened creativity supports the idea that creativity is associated with activated neural networks.”
Ueno K, et al (2015) - Neurophysiological basis of creativity in healthy elderly people: a multiscale entropy
approach. Clin Neurophysiol. 2015 Mar; 126(3):524-31. [Abstract]
“We observed that physically active elderly adults had better accuracy on both visuo-spatial attention and working
memory conditions relative to their sedentary counterparts. Additionally, these physically active elderly adults displayed
greater MSE values at larger time scales at the Fz electrode in both attention and memory conditions.” MSE =
Multiscale Entropy. “
Wang CH, et al (2014) - The association of physical activity to neural adaptability during visuo-spatial
processing in healthy elderly adults: A multiscale entropy analysis. Brain Cogn. 2014 Oct 29; 92C:73-83.
[Abstract]
“Thus, the "wisdom of old age' may find its neurophysiological basis in greater complexity of brain dynamics compared
to young ages.” “The results confirm the hypothesis: after a jump in the brain dynamics complexity during puberty a
linear increase with age is observed. During maturation (7-25 years), the maximum gain in complexity occurs over the
frontal associative cortex.”
Anokhin AP, et al (1996) - Age increases brain complexity. Electroencephalogr Clin Neurophysiol. 1996 Jul;
99(1):63-8. [Abstract]
Alzheimer’s Disease (AD) and Dementia: ▲
AD has 3 main effects on the EEG - 1) slowing, i.e., increased low frequency power plus reduction of mean frequency,
2) reduced complexity and increased regularity of EEG, and 3) loss of synchrony.
Labate D, et al - Complexity Analysis of Alzheimer Disease EEG Data through Multiscale Permutation
Entropy. Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012). [Free Full Text]
“A nonlinear measure of complexity, correlation dimension (D2), was calculated. Our results show an increase in D2 in
healthy individuals when the eyes are open, in keeping with an increase in information processing. Conversely, in FTLD
patients, no increase in D2 occurred in the open eyes condition, and D2 was significantly lower than that observed in
controls.”
Carlino E, et al (2014) - Nonlinear analysis of electroencephalogram in frontotemporal lobar degeneration.
Neuroreport. 2014 May 7; 25(7):496-500. [Abstract]
“Increased severity of AD was associated with decreased MSE complexity as measured by short-time scales, and with
increased MSE complexity as measured by long-time scales. MSE complexity in EEGs of the temporal and
occipitoparietal electrodes correlated significantly with cognitive function. MSE complexity of EEGs in various brain
areas was also correlated to subdomains of neuropsychiatric symptoms. MSE analysis revealed abnormal EEG
complexity across short- and long-time scales that were correlated to cognitive and neuropsychiatric assessments. The
MSE-based EEG complexity analysis may provide a simple and cost-effective method to quantify the severity of
cognitive and neuropsychiatric symptoms in AD patients.”
Yang AC, et al (2013) - Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients
with Alzheimer's disease. Prog Neuropsychopharmacol Biol Psychiatry. 2013 Dec 2; 47:52-61. [Abstract]
Anesthesia: ▲
“...there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia.”
Schartner M, et al (2015) - Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol
Induced General Anaesthesia. PLoS ONE 10(8): e0133532. [Free Full Text]
Using the novel “synchrony coalition entropy” and Kuramoto oscillator simulations the authors show “a robustly
measurable decrease in the complexity of spontaneous EEG during general anaesthesia.”
Schartner M, et al (2015) - Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol
Induced General Anaesthesia. PLoS One. 2015 Aug 7; 10(8). [Abstract]
Attention: ▲
“...the resting state is associated with near-critical dynamics, in which a high dynamic range and a large repertoire of
brain states may be advantageous. In contrast, a focused cognitive task induces subcritical dynamics, which is
associated with a lower dynamic range, which in turn may reduce elements of interference affecting task performance.”
Fagerholm ED, et al (2015) - Cascades and Cognitive State - Focused Attention Incurs Subcritical
Dynamics. The Journal of Neuroscience, March 18, 2015 • 35(11):4626–4634. [Free Full Text]
Autism: ▲
“...higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha
band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and
gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta
band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps
most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such
that where FC was elevated in ASD, complexity was reduced (and vice versa).:
Ghanbari Y, et al (2015) - Joint analysis of band-specific functional connectivity and signal complexity in
autism. J Autism Dev Disord. 2015 Feb; 45(2):444-60. [Abstract]
“Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the
occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with
clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis
of ECT, playing key roles in ASD pathogenesis.”
Okazaki R, et al (2015) - Changes in EEG complexity with electroconvulsive therapy in a patient with
autism spectrum disorders: a multiscale entropy approach. Front Hum Neurosci. 2015 Feb 26; 9:106. [Free
Full Text]
Cerebral Palsy: ▲
“...a definitely higher delta and lower theta and alpha powers, and higher EEG complexity in CP patients.”
Sajedi F, et al (2013) - Linear and nonlinear analysis of brain dynamics in children with cerebral palsy. Res
Dev Disabil. 2013 May; 34(5):1388-96. [Abstract]
Chronic Fatigue Syndrome: ▲
“... energy values of δ, θ, and α1 waves significantly increased in the observation group... in the right frontal and left
occipital regions... was more significant ... the correlation dimension in the observation group was significantly lower
than the control group, suggesting decreased EEG complexity in CFS patients.
Wu T, et al (2016) - Electroencephalogram characteristics in patients with chronic fatigue syndrome.
Neuropsychiatr Dis Treat. 2016; 12: 241–249. [Free Full Text]
Cognition, Mild Impairment: ▲
“...complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and
theta bands compared with control subjects, and at the theta band this reduction is more pronounced in the whole
brain and intra left hemisphere.”
Ahmadlou M, et al (2014) - Complexity of functional connectivity networks in mild cognitive impairment
subjects during a working memory task. Clin Neurophysiol. 2014 Apr; 125(4):694-702. [Abstract]
“These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive
biomarkers for detection of MCI and early AD.”
McBride JC, et al (2014) - Spectral and complexity analysis of scalp EEG characteristics for mild cognitive
impairment and early Alzheimer's disease. Comput Methods Programs Biomed. 2014 Apr; 114(2):153-63.
[Abstract]
Coma, Pediatric: ▲
Children who had a poor outcome following brain injury secondary to cardiac arrest, TBI or stroke, had ... a lower
spatial complexity of the synchrony patterns and a lower temporal variability of the synchrony index values at 15 Hz
when compared to those patients with a good outcome.
Nenadovic V, et al (2014) - Phase synchronization in electroencephalographic recordings prognosticates
outcome in paediatric coma. PLoS One. 2014 Apr 21; 9(4): e94942. [Abstract]
Consciousness: ▲
“We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most
reliable signatures of the conscious state. When combined, these measures synergize to allow an automatic
classification of patients' state of consciousness.”
Sitt JD, et al (2014) - Large scale screening of neural signatures of consciousness in patients in a
vegetative or minimally conscious state. Brain. 2014 Aug; 137(Pt 8):2258-70. [Abstract]
“These studies invariably show that the complexity of the cortical response to TMS collapses when consciousness is
lost during deep sleep, anesthesia and vegetative state following severe brain injury, while it recovers when
consciousness resurges in wakefulness, during dreaming, in the minimally conscious state or locked-in syndrome.”
Sarasso S, et al (2014) - Quantifying cortical EEG responses to TMS in (un)consciousness. Clin EEG
Neurosci. 2014 Jan; 45(1):40-9. [Abstract]
“1) A group of neurons can contribute directly to conscious experience only if it is part of a distributed functional cluster
that achieves high integration in hundreds of milliseconds. 2) To sustain conscious experience, it is essential that this
functional cluster be highly differentiated, as indicated by high values of complexity.” ...”A strong prediction based on
our hypothesis is that the complexity of the dynamic
core should correlate with the conscious state of the subject.”
Tononi G, et al (1998) - Consciousness and Complexity. Science Vol 282, 4 Dec 1998, 1846-1851.
Creativity: ▲
“Considering the general "loss of complexity" theory of aging, our finding of increased EEG complexity in elderly people
with heightened creativity supports the idea that creativity is associated with activated neural networks.”
Ueno K, et al (2015) - Neurophysiological basis of creativity in healthy elderly people: a multiscale entropy
approach. Clin Neurophysiol. 2015 Mar; 126(3):524-31. [Abstract]
“Higher frontal EEG complexity during divergent than convergent thinking could be the result of the concurrent
activation of a greater number of independently oscillating processing units.”
Mölle M, et al (1999) - EEG complexity and performance measures of creative thinking. Psychophysiology.
1999 Jan; 36(1):95-104. [Abstract]
Depression: ▲
“Frontal delta power predicted psychological pain while controlling for depressive symptoms, with participants who
exhibited less power experiencing greater psychological pain. Frontal fractal dimension asymmetry, a nonlinear
measure of complexity, also predicted psychological pain, such that greater left than right complexity was associated
with greater psychological pain. Frontal alpha asymmetry did not contribute unique variance to any regression model of
psychological pain.”
Meerwijk EL, et al (2015) - Resting-state EEG delta power is associated with psychological pain in adults
with a history of depression. Biol Psychol. 2015 Feb; 105:106-14. [Abstract]
“... aberrant functional connectivity underlies the pathophysiology of depression, which engenders abnormal
electroencephalogram (EEG) complexity. ...The decrease in EEG complexity with ECT might be a result of amelioration
of functional connectivity in the brain of a depressed patient.”
Okazaki R, et al (2013) - Effects of electroconvulsive therapy on neural complexity in patients with
depression: report of three cases. J Affect Disord. 2013 Sep 5; 150(2):389-92. [Abstract]
Down Syndrome: ▲
“The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which
indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of
the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an
inverse pattern (left frontal laterality). ...the higher EEG fractality in DS is associated with the higher fractality in the low
frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the
frontal regions.”
Hemmati S, et al (2013) - Down syndrome's brain dynamics: analysis of fractality in resting state. Cogn
Neurodyn. 2013 Aug; 7(4):333-40. [Abstract]
EEG Recording / Neurofeedback: ▲
“complexity was lower for eyes closed than for eyes open conditions.”
Ibáñez-Molina AJ, et al (2015) - Multiscale Lempel-Ziv complexity for EEG measures. Clin Neurophysiol.
2015 Mar; 126(3):541-8. [Abstract]
“The results showed significantly greater FDs in females compared to males in all brain regions except in lateral and
occipital lobes. This indicates a higher complexity of the brain dynamics in females relative to males. ...The results
showed that delta, alpha, and beta bands are the frequency bands that contribute most to the gender differences in
brain complexity. Furthermore, the lateralization analysis showed the leftward lateralization of complexity in females is
greater than in males.”
Ahmadi K, et al (2013) - Brain activity of women is more fractal than men. Neurosci Lett. 2013 Feb 22; 535:7-
11. [Abstract]
“...the complexity level increased from eyes closed to eyes open condition; and further increased in the case of 3D as
compared to 2D game play.”
Khairuddin HR, et al (2013) - Analysis of EEG signals regularity in adults during video game play in 2D and
3D. Conf Proc IEEE Eng Med Biol Soc. 2013; 2013:2064-7. [Abstract]
Executive Functioning: ▲
”... participants who suffer from poor inhibitory control can efficiently improve their performance with 10min of electrical
stimulation, and such cognitive improvement can be effectively traced back to the complexity within the EEG signals via
MSE analysis, thereby offering a theoretical basis for clinical intervention via tDCS for deficits in inhibitory control.”
Liang WK, et al (2014) - Revealing the brain's adaptability and the transcranial direct current stimulation
facilitating effect in inhibitory control by multiscale entropy. Neuroimage. 2014 Apr 15; 90:218-34. [Abstract]
Epilepsy: ▲
“...the complexity of EEG signals in the ictal state are decreased, apparently mainly over the frontal and central regions,
Weng WC, et al (2015) - Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood
Absence Epilepsy. PLoS One. 2015 Aug 5; 10(8):e0134083. [Free Full Text]
“Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may
characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. …there was a
statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all
64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG
channels as well.”
Bob P, et al (2014) - Preictal dynamics of EEG complexity in intracranially recorded epileptic seizure: a
case report. Medicine (Baltimore). 2014 Nov; 93(23):e151. [Abstract]
Hypoglycemia: ▲
“A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the
EEG long-range temporal correlations.”
Fabris C, et al (2014) - Hypoglycemia-related electroencephalogram changes assessed by multiscale
entropy. Diabetes Technol Ther. 2014 Oct; 16(10):688-94. [Abstract]
Mental Illness: ▲
“that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general
framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic
neuronal activity.”
Yang AC, et al (2013) - Is mental illness complex? From behavior to brain. Prog Neuropsychopharmacol Biol
Psychiatry. 2013 Aug 1; 45:253-7. [Abstract]
Obsessive Compulsive Disorder (OCD): ▲
“…patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations.
Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders that exhibited a
decreased complexity (such as anorexia nervosa and panic disorder).”
Aydin S, et al (2015) - Classification of obsessive compulsive disorder by EEG complexity and
hemispheric dependency measurements. Int J Neural Syst. 2015 May; 25(3):1550010. [Abstract]
Parkinson’s Disease: ▲
“...increased biosignal complexity, as revealed by MSE analysis, was found in patients with PD during non-REM sleep
at high TSFs. This finding might reflect a compensatory mechanism for early defects in neuronal network control
machinery in PD.” (MSE = Multiscale Entropy)
Chung CC. et al (2013) - Multiscale entropy analysis of electroencephalography during sleep in patients
with Parkinson disease. Clin EEG Neurosci. 2013 Jul; 44(3):221-6. [Abstract]
Pediatrics: ▲
“Based on the hypothesis that EEG-derived complexity increases with neurophysiological maturation as supported by
previously published research, SSC (skin-to-skin contact) accelerates brain maturation in healthy preterm infants as
quantified by time series measures of predictability when compared to a similar non-SSC group.”
Kaffashi F, et al (2013) - An analysis of the kangaroo care intervention using neonatal EEG complexity: a
preliminary study. Clin Neurophysiol. 2013 Feb; 124(2):238-46. [Abstract]
PTSD: ▲
Live Complexity Training neurofeedback (LCT) stresses the use of the live qEEG spectral data to signal and train
awareness of state changes such as rumination. “Repetitive and anticipatory rumination should be assessed in the
context of comorbid PTSD and MDD and interventions should focus on reducing these rumination subtypes.”
Roley ME, et al (2015) - The relationship between rumination, PTSD, and depression symptoms. J Affect
Disord. 2015 Jul 15; 180:116-21. [Abstract]
“PTSD patients have globally reduced complexity in their EEG waveforms. This study supports the hypotheses that
PTSD patients exhibit disturbed cortical information processing, and that non-linear dynamical analysis of the EEG can
be a tool for detecting changes in neurodynamics of the brain in PTSD.”
Chae JH, et al (2004) - Dimensional complexity of the EEG in patients with posttraumatic stress disorder.
Psychiatry Res Neuroimaging. 2004 May 30; 131(1):79-89. [Abstract]
Schizophrenia: ▲
“multiscale entropy measures identified abnormal dynamical EEG signal complexity in anterior brain areas in
schizophrenia that normalized selectively in fronto-central areas with antipsychotic treatment.”
Takahashi T, et al (2010) - Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia:
a multiscale entropy analysis. Neuroimage. 2010 May 15; 51(1):173-82. [Abstract]
“It was found that, compared to the controls, anterior alpha Omega and dimensional complexity are higher in
schizophrenia patients (p<0.05) with the single channel local omega complexity differentials (LCD) also increasing at
FP1, FP2, F7 and F8 electrodes. Furthermore, higher left hemisphere dimensional complexity and LCD at T3 point was
also found. The results suggest there is lower connectivity in the pre-frontal and left temporal regions with respect to
the alpha band in schizophrenia patients.”
Peng H, et al (2013) - A study on validity of cortical alpha connectivity for schizophrenia. Conf Proc IEEE
Eng Med Biol Soc. 2013; 2013:3286-90. [Abstract]
Sleep: ▲
“…activated brain states-waking and rapid eye movement (REM) sleep are characterized by higher LZC compared with
non-rapid eye movement (NREM) sleep.”
Abásolo D, et al (2015) - Lempel-Ziv complexity of cortical activity during sleep and waking in rats. J
Neurophysiol. 2015 Apr 1; 113(7):2742-52. [Abstract]
“After CPAP treatment, FD of EEG in non-rapid eye movement (NREM) sleep decreased significantly (P < 0.05), while
FD of EEG increased in rapid eye movement (REM) sleep.” FD means fractal dimension and is a measure of
entropy/complexity.
Zhang C, et al (2015) - The effect of CPAP treatment on EEG of OSAS patients. Sleep Breath. 2015 Mar 14.
[Abstract]
“Children with SDB (sleep disordered breathing) showed less complex EEG dynamics in non-REM sleep that was
unrelated to the respiratory phase. In REM sleep normal children showed a respiratory phase-related reduction in EEG
variability during the expiratory phase compared to inspiration, which was not apparent in children with SDB.”
Immanuel SA, et al (2014) - Symbolic dynamics of respiratory cycle related sleep EEG in children with
sleep disordered breathing. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014:6016-9. [Abstract]
Sleepiness: ▲
“Without daytime sleepiness (WDS) group presented more complexity than excessive daytime sleepiness (EDS) in the
occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients
than in WDS.”
Melia U, et al (2015) - Mutual information measures applied to EEG signals for sleepiness
characterization. Med Eng Phys. 2015 Mar; 37(3):297-308. [Abstract]
The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear
coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group.
Melia U. et al (2014) - Correntropy measures to detect daytime sleepiness from EEG signals. Physiol Meas.
2014 Oct; 35(10):2067-83. [Abstract]
Stroke: ▲
“Perilesional tissue exhibited a general slowing of the power spectrum(increased delta/theta, decreased beta) as well
as a reduction in MSE.” - Multi-Scale Entropy.
Chu RK (2015) - MEG-based detection and localization of perilesional dysfunction in chronic stroke. Neuroimage
Clin. 2015 Apr 8; 8:157-69. [Free Full Text]
Higuchi Fractal Dimensionality “decrease was associated to alpha increase and beta decrease of oscillatory activity
power. ... This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional
abilities.”
Zappasodi F, et al (2014) - Fractal dimension of EEG activity senses neuronal impairment in acute stroke.
PLoS One. 2014 Jun 26; 9(6):e100199. [Free Full Text]
General References:
[1] Bejan A, et al (2012) - Design in Nature. Viking.
[2] Carhart-Harris RL, et al (2014) - The entropic brain - A theory of conscious states informed by
neuroimaging research with psychedelic drugs. Frontiers in Human Neuroscience, 03 February 2014. [Free
Full Text]
[3] Yang AC, et al (2013) - Is mental illness complex - From behavior to brain. Progress in Neuro-
Psychopharmacology & Biological Psychiatry. 45 (2013) 253-257. [Abstract]
[4] Schartner M, et al (2015) - Complexity of Multi-Dimensional Spontaneous EEG Decreases during
Propofol Induced General Anaesthesia. PLoS ONE 10(8): e0133532. [Free Full Text]
[5] Ulrich G (2013) - The Theoretical Interpretation of Electroencephalography. BMed.