Editorial: EEG Phenomenology and Multiple Faces of Short-term EEG Spectral Pattern
Al. A Fingelkurts 1, *, An. A Fingelkurts 1
Identifiers and Pagination:Year: 2010
First Page: 111
Last Page: 113
Publisher ID: TONIJ-4-111
Article History:Electronic publication date: 8/9/2010
Collection year: 2010
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
An electroencephalogram (EEG) signal is extremely nonstationary, highly composite and very complex, all of which reflects the underlying integral neurodynamics. Understanding the EEG “grammar”, its internal structural organization would place a “Rozetta stone” in researchers’ hands, allowing them to more adequately describe the information processes of the brain in terms of EEG phenomenology. This Special Issue presents a framework where short-term EEG spectral pattern (SP) of a particular type is viewed as an information-rich event in EEG phenomenology. It is suggested that transition from one type of SP to another is accompanied by a “switch” between brain microstates in specific neuronal networks, or in cortex areas; and these microstates are reflected in EEG as piecewise stationary segments. In this context multiple faces of a short-term EEG SP reflect the poly-operational structure of brain activity.
“The measurement, interpretation and analysis of EEG signals depend on a specific technology that has evolved since Berger’s time”. Shaw J.C. .
NEED FOR A NEW FRAMEWORK
Thirteen years ago Bullock  wrote “We are in a primitive stage of looking at the time series of wide-band voltages in the compound, local field, potentials and of choosing descriptors that discriminate appropriately among brain loci, states (functions), stages (ontogeny, senescence), and taxa (evolution).” Today, in the year 2010, the situation is not very different although some progress has been done. The reason for such an unfortunate state of affairs is as follows: the vast majority of neuroscientists are not studying (not looking at) the electroencephalogram – EEG (time series) as a “subject” of investigation, instead they just use it as an objective tool for clinical purposes or to elucidate neurophysiological processes of cognitive activity. However, sparse studies demonstrated that the EEG signal as a neurophysiological phenomenon has its own structure 1 and rules of organization [4-8] (for the reviews see [9-12]). Only when one knows these characteristics, it is possible to make proper use of EEG as a tool and to give adequate interpretations of the obtained data. Another reason for the lack of studies of the EEG signal itself is related to fashion in science and was expressed by Shaw : “A majority of present-day scientists … prefer to use the most recent technological facilities, on the assumption that, by doing so, they may gain a lead over competitors. It is almost general practice today that a scientist, when drafting his project, is guided by the contingency of the tools he has at his hands, rather than to develop at first a genuine and definite plan …. This holds even more true for brain research, which seems to be influenced more by the latest available tools and by technological developments, rather than by personal ideas.”
In this context Basar’s call for a new framework for Neuroscience and related new approaches for integrative brain activity  is still relevant. What can this framework look like? The main tenet of such a framework should be the notion that any system (EEG in our case) has a particular structure or organization i.e. the type of connections between the elements of a system. Therefore, the main task of EEG research should be the revealing of the content of the system as having a structure (for a discussion see ).
EEG PHENOMENOLOGY FRAMEWORK
Recent advancements in Neuroscience have established several observations important for EEG phenomenology, - these are:
- EEG is characterized by several types of natural rhythmic oscillatory activity in various frequency ranges: delta (0.1-3.5 Hz), theta (4-7.5 Hz), alpha (8-13 Hz), beta (14-30 Hz) and gamma (>30 Hz) [15, 16] which reflect neurophysiological processes at different temporal scales.
- EEG rhythms are information-rich signs (telltale measures) of the underlying neurodynamics on the one hand and the signals (that is, they exert causal influence) for neuronal assemblies on the other [2, 17].
- The frequency bands of the various EEG oscillations are kept relatively constant throughout mammalian evolution, even though the numbers of neurons and their connections have increased enormously .
- All EEG oscillations exhibit high heritability thus being determined genetically (for a review and meta-analysis, see ).
- All brain areas react to sensitive and cognitive inputs with EEG oscillatory activity within almost invariant and general governing frequency bands . Experimental results show that the degree of synchrony, amplitude, duration, and phase lag continuously vary, but similar EEG oscillations are always present in the activated brain tissues . Thus, EEG frequency generators are selectively distributed across the entire brain.
- Types of neurons do not play a major role for frequency tuning of oscillatory networks. The neural architectonics of the cerebellar cortex, cerebellum, and hippocampus are completely different. In spite of this, all these structures behave with almost similar frequency responses .
- Neurophysiologically different EEG frequencies appear to be related to the timing of different neuronal assemblies, which are associated with different types of sensory and cognitive processes  thus being of fundamental importance for mediating and distributing “higher-level” processes in the human brain [21-24].
- Functions in the brain are manifested by varied degrees of superpositions of oscillations in EEG frequency ranges [13, 25].
Taking these observations together suggests that EEG oscillations are important rhythmic electrical events in the brain and may be considered as the “building blocks” of EEG phenomenology.
The history of EEG oscillations studies has demonstrated that they can be quantified successfully by EEG power spectrum. EEG power spectrum is a compact integrative real measure of the spectral energy (i.e., the energy per unit frequency interval). In neurophysiological terms EEG power spectrum reflects the strength of neuronal assemblies’ activity .
It is well known that EEG power spectrum exhibits high variability. Indeed, it was found that the power variability of the main EEG spectral components for sequential short (5-10 sec) EEG segments is 50-100% . For a long time this variability was considered as measurement error, noise or stochastic fluctuations. However, later studies [5, 28] (for the review see ) showed that in the phenomenon of EEG spectral variability, not only the stochastic fluctuations of the EEG parameters, but also the temporal structure of the signal is reflected. It is suggested that temporal structure of EEG signal is determined by a sequence of relatively stable brain microstates in specific neuronal networks, or cortex areas, which are reflected in EEG as piecewise stationary segments [5, 29, 30] (for the review see ). Functionally, each piecewise stationary EEG segment corresponds to a particular brain operation in a given cortex area [32, 33]. Therefore, segmental structure of the EEG signal reflects the poly-operational structure of brain activity.
In this context the identity of each type of EEG segment can be characterized by a particular type of short-term spectral pattern (SP) [7, 8, 34, 35]. Therefore, EEG spectral variability is functional: fluctuations in individual short-term SPs reflect fluctuations in underlying neuronal states (see contribution of Fingelkurts and Fingelkurts to this Special Issue).
Taken together these observations suggest that calculation of short-term EEG SPs is a more adequate measure of dynamic EEG oscillations than averaged power spectrum, which presents a “static” picture and tells nothing of the EEG temporal structure (see contribution of Fingelkurts and Fingelkurts to this Special Issue). Therefore, reduction of the signal to the elementary spectra (SPs) of various types in accordance with the number of types of EEG stationary segments instead of using averaged power spectrum for the same EEG is justified.
In this sense short-term EEG SPs of a given type by itself become the phenomenon that characterises the EEG. Following this approach, a phenomenology of EEG can be developed, by means of which different types of short-term SPs are considered to be characteristic for certain states of local EEGs (see contribution of Fingelkurts and Fingelkurts to this Special Issue).
Conceptual meaning of short-term EEG SP of a given type, the functional significance of its morphology and functional relevance of the parameters of the composition of short-term EEG SP types has been demonstrated. This also includes their percent ratio and the peculiarities of SP type alternation in the analyzed EEG in accordance with the changes of functional brain state, cognitive tasks and with different neuropsychopathologies (see contribution of Fingelkurts and Fingelkurts to this Special Issue).
An important, although relatively less studied, aspect of short-term SPs is the temporal variability and individual differences in the cortical distribution of short-term spectral power at different frequency bands and association of this distribution with different personality traits. These phenomena are presented in the experimental study by Knyazev (see this Special Issue). This work is of prominence since it was suggested that a particular topographic distribution of the EEG spectral power along the antero-posterior cortical axis can be viewed as a "fingerprint" of sort which enables researchers to distinguish individuals.
Another important aspect of short-term EEG SPs that has not yet been addressed is their functional role during extreme condition within a continuum of possible conditions, – in this case – anesthesia. An experimental study presented by Ozgoren (see this Special Issue) demonstrated changes in the types of short-term SPs during the administration of anesthesia. This study is very important in providing a useful model for the study of short-term spectral phenomena of the brain in relation to loss and gain of consciousness.
AIM OF THE SPECIAL ISSUE
The objective of this Special Issue is aimed at providing a current update on the relation between a power spectrum computed from short epochs of ongoing EEG and the actual state of the neuronal assemblies in the underlying network.
Contributions from different experts in the field provide experimental studies and a detailed review of this challenging frontier of neuroscience. We hope this Special Issue will help interested researchers become familiar with research achievements and new directions.
1 Structure – totality of elemental units and interrelations between them, according to which this totality forms a single entity with its own spatial-temporal definiteness .
|||Shaw JC. The brain’s akpha rhythms and the mind. Amsterdam: Elsevier 2003.
|||Bullock TH. Signals and signs in the nervous system: The dynamic anatomy of electrical activity Proc Natl Acad Sci USA 1997; 94: 1-6.|
|||Serjantov VF. Principle of structure and it meaning for physiology. Voprosi Dialekticheskogo Materializma and Teoretucheskoi medicine (Questions of Dialectic Materialism and Theoretical Medicine) Leningrad 1962; 91-136.|
|||Bodunov MV. The EEG “alphabet”: the typology of human EEG stationary segments In: Rusalov VM, Ed. Individual and psychological differences and bioelectrical activity of human brain. Moscow: Nauka (In Russian) 1988; pp. 56-70.|
|||Jansen BH, Cheng W-K. Structural EEG analysis: an explorative study Int J Biomed Comput 1988; 23: 221-37.
|||Nunez PL. Toward a quantitative description of large-scale neocortical dynamic function and EEG Behav Brain Sci 2000; 23(3): 371-437.
|||Fingelkurts AlA, Fingelkurts AnA, Kaplan AYA. The regularities of the discrete nature of multi-variability of EEG spectral patterns Int J Psychophysiol 2003; 47(1): 23-41.
|||Fingelkurts AlA, Fingelkurts AnA, Krause CM, Kaplan AYA. Systematic rules underlying spectral pattern variability: Experimental results and a review of the evidences Int J Neurosci 2003; 113: 1447-73.
|||Kaplan AYA, Shishkin SL. Application of the change-point analysis to the investigation of the brain’s electrical activity In: BE Brodsky, BS Darhovsky, Eds. Non-Parametric statistical diagnosis Problems and methods. Dordrecht: Kluwer Acadamic Publishers 2000; pp. 333-88.
|||Kaplan AYA, Fingelkurts AnA, Fingelkurts AlA, Borisov SV, Darkhovsky BS. Nonstationary nature of the brain activity as revealed by EEG/MEG: methodological, practical and conceptual challenges Signal Processing 2005; 85(11): 2190-2.
||| Fingelkurts AnA, Fingelkurts AlA. Operational Architectonics of the human brain biopotential field: Towards solving the mind-brain problem Brain Mind 2001; 2(3): 261-96. Available at http://www.bm-science.com/team/art18.pdf
|||Fingelkurts ANA, Fingelkurts AlA. Brain-mind Operational Architectonics imaging: technical and methodological aspects Open Neuroimag J 2008; 2: 73-93.
|||Basar E, Basar-Eroglu C, Karakas S, Schurmann M. Oscillatory brain theory: a new trend in neuroscience IEEE Eng Med Biol Mag 1999; 18(3): 56-66.
|||Pavlova LP, Romanenko AF. Systemic approach to psychophysiological study of human brain Leningrad: Nauka 1988; 209|
|||The International Federation of Societies for Electroencephalography and Clinical Neurophysiology. A glossary of terms most commonly used by clinical electroencephalographers. Electroencephalogr Clin Neurophysiol 1974; 37: 538-48.
|||Steriade M, Gloor P, Llinás RR, Lopes de Silva FH, Mesulam MM. Report of IFCN Committee on basic mechanisms. Basic mechanisms of cerebral rhythmic activities Electroencephalogr Clin Neurophysiol 1990; 76: 481-508.
|||Achimowicz JZ, Bullock TH. Nonlinear properties of local field potentials in brain: implications for biological neural network modelling Proc Ann Res Symp Inst Neural Comput Univ California San Diego 1993; 3: pp. 29-49.|
|||Buzsáki G. Rhythms of the brain. US: Oxford University Press 2006; p. 448.
|||van Beijsterveldt CEM, van Baal GCM. Twin and family studies of the human electroencephalogram: a review and a meta-analysis Biol Psychol 2002; 61: 111-38.
|||Basar E. Oscillations in “brain-body-mind” - A holistic view including the autonomous system Brain Res 2008; 1235: 2-11.
|||Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis Brain Res Rev 1999; 29: 169-95.
|||Klimesch W. Event-related band power changes and memory performance. Event-related desynchronization and related oscillatory phenomena of the brain In: Pfurtscheller G, Lopez da, Silva FH, Eds. Handbook of electroencephalography and clinical neurophysiology. revised. Amsterdam: Elsevier l999; 6: pp. 151-78.|
|||Basar E, Basar-Eroglu C, Karakas S, Schurmann M. Gamma, alpha, delta, and theta oscillations govern cognitive processes Int J Psychophysiol 2001; 39: 241-8.
|||Basar E, Schurmann M, Demiralp T, Basar-Eroglu C, Ademoglu A. Event-related oscillations are ‘real brain responses’ – wavelet analysis and new strategies Int J Psychophysiol 2001; 39: 91-127.
|||Basar E, Ozgoren M, Karakas S, Basar-Eroglu C. Super-synergy in the brain: the grandmother percept is manifested by multiple oscillations Int J Bifurcat and Chaos 2004; 14(2): 453-91.
|||Dumermuth HG, Molinari L. Spectral analysis of the EEG. Some fundamentals revisited and some open problems Neuropsychobiol 1987; 17: 85-99.
|||Oken BS, Chiappa KH. Short-term variability in EEG frequency analysis Electroencephalogr Clin Neurophysiol 1988; 69(3): 191-8.
|||Burov IUV, Kaplan AIA. The effect of amiridin on the spectral characteristics of the human EEG Eksp Klin Farmakol Exp Clin Pharmacol 1993; 56(5): 5-8. (In Russian)|
|||Lehmann D. Brain electric microstates and cognition: the atoms of thought In: John ER, Ed. Machinery of the Mind. Birkhäuser: Boston 1990; pp. 209-4.
|||Kelso JAS. Dynamics patterns: the self-organization of brain and behaviour. USA: MIT Press 1995.
|||Fingelkurts ANA, Fingelkurts AIA. Timing in cognition and EEG brain dynamics: discreteness versus continuity Cogn Process 2006; 7(3): 135-62.
|||Fingelkurts ANA, Fingelkurts AlA. Making complexity simpler: Multivariability and metastability in the brain Int J Neurosci 2004; 114: 843-62.
|||Fingelkurts ANA, Fingelkurts AIA. Mapping of the brain operational architectonics In: Chen FJ, Ed. Focus on Brain Mapping Research 2005; 59-98. Available at http://www.bm-science.com/team/chapt3.pdf|
|||Gevins AS. Analysis of the electromagnetic signals of the human brain: Milestones, obstacles, and goals IEEE Trans Biomed Eng 1984; 31: 833-50.|
|||Gevins AS. Quantitative human neurophysiology In: Hannay HJ, Ed. Experimental techniques in human neuropsychology. New York: Oxford Press 1986; pp. 419-56.|