.
`Ž
`Brain Research Reviews 29 1999 169–195
`
`Full-length review
`EEG alpha and theta oscillations reflect cognitive and memory
`performance: a review and analysis
`Wolfgang Klimesch )
`Department of Physiological Psychology, Institute of Psychology, UniÕersity of Salzburg, Hellbrunnerstr. 34, A-5020 Salzburg, Austria
`Accepted 24 November 1998
`
`Abstract
`
`Evidence is presented that EEG oscillations in the alpha and theta band reflect cognitive and memory performance in particular. Good
`Ž .
`Ž .
`performance is related to two types of EEG phenomena i a tonic increase in alpha but a decrease in theta power, and ii a large phasic
`Ž
`.
`event-related decrease in alpha but increase in theta, depending on the type of memory demands. Because alpha frequency shows large
`interindividual differences which are related to age and memory performance, this double dissociation between alpha vs. theta and tonic
`vs. phasic changes can be observed only if fixed frequency bands are abandoned. It is suggested to adjust the frequency windows of alpha
`and theta for each subject by using individual alpha frequency as an anchor point. Based on this procedure, a consistent interpretation of a
`Ž
`variety of findings is made possible. As an example, in a similar way as brain volume does, upper alpha power increases but theta power
`.
`decreases from early childhood to adulthood, whereas the opposite holds true for the late part of the lifespan. Alpha power is lowered
`and theta power enhanced in subjects with a variety of different neurological disorders. Furthermore, after sustained wakefulness and
`during the transition from waking to sleeping when the ability to respond to external stimuli ceases, upper alpha power decreases, whereas
`Ž
`.
`theta increases. Event-related changes indicate that the extent of upper alpha desynchronization is positively correlated with semantic
`long-term memory performance, whereas theta synchronization is positively correlated with the ability to encode new information. The
`reviewed findings are interpreted on the basis of brain oscillations. It is suggested that the encoding of new information is reflected by
`Ž
`.
`theta oscillations in hippocampo-cortical feedback loops, whereas search and retrieval processes in semantic long-term memory are
`reflected by upper alpha oscillations in thalamo-cortical feedback loops. q 1999 Elsevier Science B.V. All rights reserved.
`
`Keywords: EEG; ERD; Alpha; Theta; Oscillation; Memory; Hippocampus; Thalamus
`
`Contents
`1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.1. Cognitive and memory performance: introductory remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.2. Alpha and theta: some basic aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.3. Definition of frequency bands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.4. Oscillatory components in the EEG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2. Tonic changes and differences in the alpha and theta frequency range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.1. Age related changes and performance related differences in alpha frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.2. Age related changes in alpha and theta power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.3. Age related changes in alpha and theta reactivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.4. Changes in alpha and theta power during the wake–sleep cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.5. Alpha power in congenital blindness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`3. Interim discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`4. Event-related phasic changes in the alpha and theta band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`Ž
`.
`4.1. Desynchronization in the lower alpha band reflects attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`4.2. Desynchronization in the upper alpha band reflects semantic memory performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`4.3. Synchronization in the theta band reflects episodic memory and the encoding of new information . . . . . . . . . . . . . . . . . . . . . . .
`4.4. The relationship between desynchronization, synchronization and absolute power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
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`
`) Fax: q43-662-8044-5126; e-mail: wolfgang.klimesch@sbg.ac.at
`
`0165-0173r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.
`Ž
`.
`PII: S0165-0173 98 00056-3
`
`Wave Neuroscience EX2002
`PeakLogic v. Wave Neuroscience
`IPR2022-01550
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`

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`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
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`5. General conclusions and physiological considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
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`191
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`
`1. Introduction
`
`In a physiological sense, EEG power reflects the num-
`ber of neurons that discharge synchronously. Because brain
`volume and the thickness of the cortical layer is positively
`w
`x.
`Ž
`correlated with intelligence
`e.g., Refs. 12,165
`it
`is
`tempting to assume that EEG power too, is a measure that
`reflects the capacity or performance of cortical information
`processing. Although it will be argued that
`this is in
`principle the case,
`it must be emphasized that power
`measurements are strongly affected by a variety of unspe-
`cific factors such as the thickness of the skull or the
`volume of cerebrospinal fluid, by methodological and tech-
`Ž
`nical factors
`such as interelectrode distance or type of
`.
`montage but also by more specific factors such as age,
`arousal and the type of cognitive demands during actual
`task performance.
`It is the purpose of the present article to show that EEG
`power is indeed related to cognitive and memory perfor-
`mance, but in a complex and partly non-linear way. Within
`the alpha frequency range EEG power is positively related
`to cognitive performance and brain maturity, whereas the
`opposite holds true for the theta frequency range. Alpha
`and theta reactivity as well as event-related changes in
`alpha and theta band power show yet another pattern of
`results. During actual task demands the extent of alpha
`power suppression is positively correlated with cognitive
`Ž
`.
`performance
`and memory performance in particular
`whereas again the opposite holds true for the theta band.
`Here, the extent of theta synchronization is related to good
`performance. The review which focuses on the theta and
`alpha frequency range considers first
`tonic changes in
`Ž
`.
`power
`such as age related differences in the EEG and
`then phasic or event-related changes.
`
`1.1. CognitiÕe and memory performance: introductory re-
`marks
`
`Two basic aspects of memory processes will be distin-
`w
`x
`guished 66 . The first refers to processes of the working
`Ž
`.
`memory system WMS , the second to that of the long-term
`Ž
`.
`memory system LTMS . Probably any cognitive process
`depends on the resources of both systems. As an example,
`let us consider an every day cognitive process such as
`recognizing a familiar object. The basic idea here is that
`after a sensory code is established, semantic information in
`Ž
`.
`long-term memory LTM is accessed which is used to
`identify the perceived object. If the matching process
`
`yields a positive result, the object is recognized which in
`Ž
`.
`turn leads to the creation of a short-term memory STM
`code. In this case, bottom up pathways are activated which
`are similar or identical to those which would serve to
`retrieve information from LTM. This classical explanation
`of encoding still reflects the current view, which was
`w
`x
`originally stated by Shiffrin and Geisler 137 : ‘‘The pro-
`cess of encoding is essentially one of recognition:
`the
`appropriate image or feature is contacted in LTM and then
`Ž
`.
`Ž
`.
`placed i.e., copied in STM’’ p. 55 . Complex cognitive
`processes such as speaking and thinking may also be
`described in terms of a close interaction between the WMS
`and LTMS. The basic difference to the foregoing example
`is that a sensory code is lacking and that a code is
`generated in STM which in the case of speaking represents
`a ‘plan’ of what to say. The codes generated in STM
`trigger search processes in LTM to retrieve the relevant
`knowledge about the appropriate semantic, syntactic and
`articulatory information. This latter idea is similar to Bad-
`deley’s concept of working memory, which comprises an
`attentional controller, the central executive and subsidiary
`w
`x
`slave systems 3,4 .
`In the WMS, encoding has two different meanings, one
`refers to episodic, the other to sensory-semantic informa-
`Ž
`tion. The encoding of sensory information as a process of
`.
`recognition always aims at the semantic understanding of
`perceived information which is first processed in the
`LTMS. Because of this close relation between sensory and
`semantic encoding we will use the term sensory-semantic
`code. The creation of a new code comprises episodic
`w
`x.
`Ž
`information, which according to Tulving e.g., 158 , is
`that type of contextual information which keeps an individ-
`ual autobiographically oriented within space and time.
`Because time changes the autobiographical context perma-
`nently, there is a permanent and vital need to update and
`store episodic information. Thus, the formation of episodic
`memory traces is one of the most important tasks of the
`WMS.
`Cognitive performance is closely related and linked to
`the performance of the WMS and LTMS. As an example,
`most intelligence tests comprise subtests measuring mem-
`Ž
`.
`ory span an important function of the WMS and tests
`Ž
`such as judging analogies which reflect an important
`.
`aspect of semantic LTMS . The increase in cognitive
`performance from childhood to puberty as well as the
`decrease in performance during the late lifespan is, most
`likely, due or at least closely linked to performance changes
`in the WMS and LTMS.
`
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`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
`)
`(
`
`171
`
`to the functional anatomy of memory,
`With respect
`there is good evidence that brain structures that lie in the
`Ž
`medial temporal lobe comprising the hippocampal forma-
`.
`tion and prefrontal cortex support various functions of the
`w
`x
`Ž
`WMS cf. the reviews in Refs. 102,140,141 and findings
`about the contribution of the hippocampal region for the
`generation of event-related potentials in the human scalp
`w
`x.
`EEG during novelty detection 85,86 . Studies focusing on
`the ontogeny of human memory indicate that
`the hip-
`pocampus matures relatively early in postnatal life, whereas
`the prefrontal cortex which is important for the develop-
`Ž
`ment of an increased memory span matures much later cf.
`w
`x
`.
`Refs. 109,57 for reviews . Although many aspects of
`Ž
`memory develop early in childhood up to an age of about
`.
`2 or 3 years it is not yet known when memory is fully
`matured. As will be discussed in Section 5, there is good
`Ž
`evidence that a complex structure of feedbackloops or
`w
`x.
`connecting the hippocampus with
`‘reentrant loops’; 37
`different cortical regions and the prefrontal cortex in par-
`ticular may provide the anatomical basis for the WMS. It
`appears likely that these feedbackloops develop and be-
`come increasingly differentiated with increasing age over
`the entire time span of childhood and possibly early adult-
`hood. The increase in EEG frequency during that
`life
`Ž
`.
`period may besides other factors reflect this process of
`brain maturation. The basic assumption is that the better
`these feedbackloops become integrated and interconnected
`with other brain areas, the faster the frequency of EEG
`oscillations will be.
`
`1.2. Alpha and theta: some basic aspects
`
`Ž
`Alpha is with the exception of irregular activity in the
`.
`delta range and below the dominant frequency in the
`human scalp EEG of adults. It is manifested by a ‘peak’ in
`Ž
`.
`spectral analysis cf. Fig. 1 and reflects rhythmic ‘alpha
`w
`x.
`Ž
`waves’ which are known since Berger e.g., Ref. 10 . The
`fact that alpha clearly is an oscillatory component of the
`human EEG has led to a recent ‘renaissance’ in the interest
`w
`x
`of EEG alpha activity 5,8,7,9 .
`Frequency and power are closely interrelated measures.
`Usually, alpha frequency is defined in terms of peak or
`gravity frequency within the traditional alpha frequency
`Ž
`.
`range f 1 to f 2 of about 7.5–12.5 Hz. Peak frequency is
`that spectral component within f 1 to f 2 which shows the
`Ž
`.
`largest power estimate cf. Fig. 1A . Alpha frequency can
`Ž
`.
`also be calculated in terms of gravity or ‘mean’
`fre-
`quency which is the weighted sum of spectral estimates,
`divided by alpha power: Ý a f =f r Ý a f
`Ž
`Ž Ž
`.
`..
`Ž
`Ž
`..
`. Power
`Ž
`.
`spectral estimates at frequency f are denoted a f . The
`index of summation is in the range of f 1 to f 2. Particu-
`Ž
`larly if there are multiple peaks in the alpha range for a
`w
`x.
`classification see e.g., Ref. 39 , gravity frequency appears
`the more adequate estimate of alpha frequency.
`As is well known from animal research, unlike alpha in
`the human scalp EEG, theta is the dominant rhythm in the
`
`.
`Ž
`Fig. 1. Individual alpha frequency IAF varies to a large extent even in a
`sample of age matched subjects as the three power spectra indicate.
`Depending on their memory performance and other factors, IAF lies in
`Ž
`.
`the range of about 9.5–11.5 Hz for young healthy adults. A If power
`spectra are calculated separately for a resting period in which subjects are
`Ž
`.
`in a state of alert wakefulness dotted line
`and during actual
`task
`Ž
`.
`performance e.g., memorizing visually presented words , alpha power
`Ž
`.
`becomes suppressed, but theta power increases bold line . That fre-
`quency where the two spectra intersect marks the transition frequency
`
`Ž .TF between the theta and alpha band. Experiments from our laboratory
`have shown that TF lies almost 4 Hz below IAF and that the lower alpha
`Ž
`.
`band which can be further divided in two subbands is somewhat wider
`Ž .
`than the upper alpha band. B Alpha power of a subject with a very slow
`alpha rhythm will fall outside the fixed frequency window of the tradi-
`Ž
`.
`tional alpha band of about 7.5–12.5 Hz. As an example, if IAF in A is
`Ž .
`Ž .
`10 Hz and in B only 8 Hz, the lower alpha band in B will already
`Ž
`.
`comprise that frequency range which is the theta band in A , whereas the
`Ž .
`upper alpha band in B will fall in the frequency range of the lower
`Ž
`.
`alpha band in A . Similar problems arise, if IAF lies already at the upper
`Ž .
`frequency limit of the alpha band. C For a subject with a very fast IAF
`of about 11.5 Hz or more, almost the entire frequency range of the
`Ž
`.
`traditional alpha band 7.5–12.5 Hz would actually fall in the range of
`the lower alpha band of that subject. It is therefore of crucial importance
`to adjust frequency bands individually for each subject, by using IAF as
`Ž
`.
`the cut off point between the lower and upper alpha band see text .
`
`

`

`172
`
`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
`)
`(
`
`hippocampus of lower mammals. Its frequency ranges
`w
`x.
`Ž
`from about 3 to 12 Hz e.g., Ref. 95 and, thus, shows a
`much wider frequency range than in humans where theta
`lies between about 4 to 7.5 Hz. Its wide frequency range
`and large power make it easy to observe frequency and
`power changes in animals. This is in sharp contrast to the
`human scalp EEG, where—without the help of sophisti-
`cated methods—changes in theta frequency are very diffi-
`cult or almost impossible to detect. The question, thus, is
`whether there is a physiological criterion that allows us to
`decide which frequency marks the transition between alpha
`and theta oscillations.
`Alpha and theta respond in different and opposite ways.
`The crucial finding is that with increasing task demands
`Ž
`theta synchronizes, whereas alpha desynchronizes cf. the
`.
`bold in relation to dotted line in Fig. 1A . This fact is
`documented in reviews on event-related desynchronization
`. w
`x
`Ž
`ERD 122,67,68 , as well as by a variety of studies using
`w
`Ž
`other experimental approaches
`e.g., Ref. 45,46,95,99,
`x.
`131,133,129 . If EEG power in a resting condition is
`compared with a test condition, alpha power decreases
`Ž
`.
`Ž
`.
`desynchronizes and theta power increases synchronizes .
`Classical findings demonstrate that the decrease in alpha
`Ž
`.
`power suppression of the alpha rhythm can be observed
`primarily when subjects close their eyes. More recent
`evidence, however, suggests that attentional and semantic
`memory demands are powerful factors which lead to a
`selective suppression of alpha in different ‘subbands’ and
`Ž
`that the well described effects of visual stimulation e.g.,
`.
`just a special class of
`eyes open vs. closed represent
`Ž
`sensory-semantic task demands
`see Section 4 and Refs.
`w
`x.
`72,75,78–81 . As already emphasized, encoding of sen-
`sory information always aims to extract the meaning of the
`perceived information which is stored in semantic LTM.
`Thus, there is a close relation between sensory and seman-
`tic encoding.
`As Fig. 1 illustrates, that frequency in the power spectra
`which marks the transition from theta synchronization to
`alpha desynchronization may be considered the individual
`Ž
`.
`transition frequency TF between the alpha and theta band
`for each subject. When using this method to estimate TF,
`we have found that TF shows a large interindividual
`Ž
`.
`variability ranging from about 4 to 7 Hz which is signifi-
`w
`x
`cantly correlated with alpha peak frequency 76 . Prelimi-
`nary evidence for a covariation between theta and alpha
`w
`x
`frequency was already found by Klimesch et al. 75 and is
`w
`x
`further documented by Doppelmayr et al. 34 . These
`Ž
`.
`findings indicate that theta frequency as measured by TF
`varies as a function of alpha frequency and suggest to use
`alpha frequency as a common reference point for adjusting
`different frequency bands not only for the alpha, but theta
`range as well. For estimating theta power, the individual
`determination of frequency bands may even be more im-
`portant because otherwise the effects of theta synchroniza-
`tion are masked by alpha desynchronization particularly in
`Ž
`.
`the range of TF cf. Fig. 1B,C .
`
`1.3. Definition of frequency bands
`
`Because alpha frequency varies to a large extent as a
`function of age, neurological diseases, memory perfor-
`w
`x
`Ž
`.
`mance see Section 2.1 below , brain volume 115,114
`w
`x
`and task demands 73 , the use of fixed frequency bands
`does not seem justified. As an example, an elderly subject
`with bad memory performance may show a peak fre-
`w
`x
`quency of 7 Hz or lower 16 . When strictly applying the
`rule that alpha peak frequency is that spectral component
`within f 1s7.5 and f 2s12. 5 Hz which shows maximal
`power, we would arrive at the conclusion that the obtained
`frequency indicates theta instead of alpha frequency. For-
`tunately, as discussed in the previous section, there is a
`physiological criterion which allows us to answer this
`question. If EEG power around 7 Hz would desynchronize
`Ž
`during a test—as compared to a resting condition cf. Fig.
`.1A —we still would accept that a peak frequency of 7 Hz
`indicates alpha but not theta frequency.
`This example documents the necessity to define the
`Ž
`alpha band individually for each subject as that range
`f 1
`.
`to f 2 ‘around’ the individual dominant EEG frequency
`Ž
`.
`above the lower delta range that desynchronizes during
`task demands. In order to avoid confusions with traditional
`measures, we use the term individual alpha frequency
`Ž
`.
`Ž
`.
`IAF to denote the individual dominant peak or gravity
`Ž
`.
`EEG frequency in the range of
`f 1 to f 2 of a single
`subject. The crucial point, of course, is the exact location
`and individual definition of the frequency limits f 1 and
`f 2. For f 1, TF is a good estimate, but for f 2 an obvious
`physiological criterion is lacking. An indirect way to solve
`this problem is to define the frequency limits of the lower
`alpha band by f 1 and IAF and to assign the ‘remaining’
`w
`Ž
`part of the alpha frequency range which equals alpha
`x
`w
`x.
`frequency window y IAFyf 1
`to the upper band. Re-
`sults from our laboratory indicate that the lower alpha
`band has a width of about 3.5–4 Hz. Accordingly, the
`Ž
`.
`upper alpha band the frequency range above IAF is a
`rather narrow band of 1 or 1.5 Hz, if it is assumed that the
`alpha frequency window has a width of about 5 Hz.
`Ž
`Experimental findings discussed in detail in Section 4
`.
`below indicate that the upper alpha band—defined as a
`band of 2 Hz above IAF—responds selectively to semantic
`LTM demands and behaves in a completely different and
`Ž
`sometimes opposite way as the lower alpha band see also
`w
`x.
`the review in Ref. 121 . Furthermore and most impor-
`Ž
`tantly, it was found that the lower alpha band a band of 4
`.
`Hz below IAF reflects different types of attentional de-
`mands. Thus, it was broken down into two subbands of 2
`Ž
`Hz each which are termed lower-1 and lower-2 alpha see
`.
`Fig. 1 and Section 4 .
`In summarizing, when using IAF as an anchor point, it
`Ž
`proved useful
`to distinguish three alpha bands with a
`.
`Ž
`.
`width of 2 Hz each , two lower alpha bands below IAF
`Ž
`.
`and one upper alpha band above IAF . The theta band is
`defined as the frequency band of 2 Hz which falls below
`
`

`

`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
`)
`(
`
`173
`
`TF. As for the upper frequency limit of the upper alpha
`band, there are no clear criteria for the lower frequency
`limit of the theta band. In any case, however, it is impor-
`tant to emphasize that the use of narrow frequency bands
`reduces the danger that frequency specific effects go unde-
`tected or cancel each other. Thus, broad band analyses
`must be interpreted with great caution. The implication is
`that an unbiased estimate of alpha and theta power can be
`obtained only, if the traditional fixed band analyses are
`abandoned and if narrow frequency bands are adjusted to
`the individual alpha frequency of each subject. However,
`the vast majority of studies use broad, instead of narrow
`Ž
`.
`alpha bands cf. Table 1 which in addition are not ad-
`justed individually.
`The suggested definition of frequency bands is based on
`Ž
`.
`physiological criteria such as TF and IAF and on the
`Ž
`functional significance of narrow frequency bands
`see
`.
`Section 4 . An alternative way to define EEG frequency
`bands is to analyze the covariance of spectral estimates by
`multivariate statistical methods such as factor analysis. The
`crucial question here is whether the activities in different
`frequency bands vary independently. With respect to the
`traditional theta and alpha frequency range, results from
`
`factor analyses show at least three independent factors, one
`for theta, one for the lower alpha band and another for the
`w
`x
`Ž
`upper alpha band see e.g., Ref. 106 and the summary in
`w
`x.
`Ref. 96 . However, the frequency limits vary consider-
`Ž
`ably between studies. As an example, Wieneke reported
`w
`x.
`found a factor covering the frequency range
`in Ref. 96
`of 6–9 Hz, which was termed ‘theta’ whereas within that
`w
`x
`same frequency range, Mecklinger et al. 106 extracted
`one component which they classified as ‘lower alpha’.
`Divergent results from factor analyses are due to the type
`Ž
`of power measurements
`spectral estimates may be ex-
`.
`pressed in terms of relative or absolute power , type of
`w
`x
`Ž
`referential , bipolar, Lapla-
`derivation e.g., monopolar
`.
`Ž
`cian , electrode location, task type resting condition with
`.
`eyes open or closed or performance of some task , the
`selected sample of subjects, and finally the method used to
`extract and rotate factors. It also should be emphasized that
`factor analyses are usually performed on the basis of a
`correlation matrix which was obtained by correlating spec-
`tral estimates over a sample of n subjects. Done in this
`way,
`the extracted factors represent average frequency
`ranges of that particular sample. However, factor analysis
`could also be used for an individual definition of fre-
`
`Der.
`M
`M
`M
`M
`
`Table 1
`ŽU .
`.
`Ž
`frequency
`EEG alpha activity: frequency limits and peak a or gravity
`Ref. no.rauthor
`Broad band
`Subbands
`Age
`a-frequency
`N
`w x1 Alloway et al.
`8–12
`–
`10
`29–62
`–
`w x2 Anokhin and Vogel
`8–13
`–
`101
`20–45
`–
`
`w x11 Besthorn et al.
`–
`7.5–9.5; 9.5–12.5
`92
`59–78
`–
`
`w x14 Boiten et al.
`8–13
`–
`8
`22–29
`–
`
`w x16 Brenner et al.
`–
`8.0–9.9; 10–12.9
`119
`51–89
`6–14,r
`
`w x17 Breslau et al.
`8.2–12.9
`–
`33
`18–78
`–
`
`w x24 Chiaramonti et al.
`8–11.5
`–
`55
`51–81
`–
`
`w x27 Coben et al.
`U
`8–13
`–
`127
`64–83
`6.9–9.3
`
`w x36 Duffy et al.
`8–11.75
`–
`63
`30–80
`7.5–13a,r
`
`w x44 Gevins et al.
`8–13
`–
`55
`–
`–
`
`w x55 Hartikainen et al.
`7.6–13.9
`–
`52
`33–78
`9.6–10.1a
`
`w x61 Jausovec
`7.5–13
`–
`60
`18–19
`–
`
`w x62 John et al.
`7.5–12.5
`–
`648
`1–21
`–
`
`w x65 Kaufman et al.
`8–12
`–
`3
`–
`–
`
`w x87 Kononen and Partanen
`¨ ¨
`7.6–13.9
`–
`54
`23–80
`–
`
`w x89 Krause et al.
`–
`8–10; 10–12
`10
`23–41
`–
`
`w x99 Marciani et al.
`8–12.5
`–
`60
`19–89
`–
`
`w x117 Obrist et al.
`–
`–
`57
`65–84
`7.5–9.2a
`
`w x124 Pfurtscheller
`–
`1 or 2 Hz bands
`M, B
`§
`§
`–
`
`w x132 Salenius et al.
`7–14 Hz
`–
`MEG
`13
`20–49
`9.3–11a,r
`
`w x134 Schmid et al.
`7.4–12.5
`–
`B
`536
`5–11
`–
`
`w x139 Somsen et al.
`7.5–12.5
`–
`M
`142
`5–12
`4.5–10a
`
`w x146 Sterman et al.
`–
`2 Hz bands 5–15
`M
`26
`25–39
`–
`
`w x153 Szelies et al.
`8–13
`–
`M
`58
`53–78
`–
`
`w x156 Torres et al.
`9.4$,S.D.s1
`–
`–
`M
`182
`48–88
`
`w x157 Torsvall and Akerstedt
`8–11.9
`–
`B
`11
`27–58
`–
`
`w x162 Wada et al.
`7.5–12.5
`–
`M
`264
`3–26
`–
`
`w x164 Wieneke et al.
`9.9a,S.D.s1
`3 db below peak
`M
`110
`18–50
`
`w x166 Williamson et al
`–
`8–13
`MEG
`§
`§
`–
`Der.sDerivation; Msmonopolar; Bsbipolar, CsCommon average reference; Nssample size; §, Reviewed data; r, range for lowest and highest
`frequency; $, Dominant frequency, defined by period of alpha wave.
`
`M
`M
`B
`M
`M
`B
`M
`B
`MEG
`B
`M
`C
`
`

`

`174
`
`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
`)
`(
`
`quency bands, if the EEG of a single subjects is used and
`if spectral estimates are correlated over a series of n trials
`Ž
`.
`epochs .
`Even if frequency bands are defined individually for
`each subject, it must be emphasized that EEG frequencies
`vary between recording sites. As an example, it is well
`known that alpha waves occur primarily during wakeful-
`ness over the posterior regions of the head and can be best
`seen with eyes closed and under conditions of physical
`w
`x
`relaxation and mental inactivity 110 . The frequency of
`alpha waves is faster at posterior and slower at anterior
`recording sites. It would,
`thus, be desirable to adjust
`frequency bands not only individually for each subject but
`also for each recording site. For practical reasons, this has
`not yet been done.
`
`1.4. Oscillatory components in the EEG
`
`is
`In an empirical sense, an oscillatory component
`defined by the presence of a rhythmic activity in the EEG
`which is manifested by a ‘peak’ in spectral analysis. In
`contrast to theta, alpha as the dominant rhythmic activity,
`characterized by sinusoidal wave forms, clearly meets this
`definition. In the human EEG of young healthy adults,
`there are at least two other oscillatory components, the mu
`Ž
`rhythm and the third rhythm. The mu rhythm mu stands
`.
`for motor has an arch-shaped wave morphology, appears
`Ž
`over the motor area and becomes suppressed desynchro-
`.
`Ž
`nized during motor related task demands see the exten-
`w
`x.
`sive work of Pfurtscheller et al., e.g., Ref. 125 . Other
`terms are ‘arcade’, ‘comb’ or ‘wicket’ rhythm, ‘central’,
`Ž
`‘rolandic’ and ‘somatosensory’ alpha cf. the review in
`w
`x
`.
`Ref. 110 p. 137f . The third rhythm which is not de-
`Ž
`tectable in the scalp EEG but e.g., by the use of epidural
`w
`x.
`electrodes or magnetoencephalography MEG is indepen-
`Ž
`.
`dent from the posterior alpha and mu rhythm and appears
`w
`x
`over the midtemporal region 110 . In emphasizing the fact
`Ž
`.
`in the MEG this rhythm is best seen over the
`that
`w
`x
`auditory cortex in the temporal lobe, Hari 51 uses the
`Ž
`.
`term ‘tau’ rhythm tau stands for temporal . There is some
`evidence that the tau rhythm becomes suppressed during
`Ž
`acoustic but not visual stimulation cf. the review in Hari
`w
`x.
`et al. 52 . Because the focus of this review is on theta and
`alpha activity with respect to cognitive performance and
`memory, the mu and third rhythm will not be considered.
`Despite the fact that theta does not meet the criteria for
`an oscillatory component
`in the EEG,
`it may still be
`argued that activity within the individually defined theta
`band reflects oscillatory processes. In a theoretical sense,
`the EEG can be conceived of a linear superposition of a set
`Ž
`.
`of different sine waves oscillatory components . In addi-
`tion, there are more specific arguments which are based on
`empirical evidence:
`1. With the help of sophisticated methods, theta peaks can
`be found in the human scalp EEG of young healthy
`w
`x
`adults 45 .
`
`Table 2
`.
`Ž
`Double dissociation between tonic and phasic event-related changes in
`alpha and theta power with respect to cognitive performance
`Increasing performance
`Decreasing performance
`Theta power Alpha power Theta power Alpha power
`Tonic change Decreases
`Increases
`Increases
`Decreases
`Phasic change Increases
`Decreases
`Decreases
`Increases
`
`Tonic changes are discussed in Section 2, phasic changes in Section 4
`below.
`A phasic change is measured as an increase or decrease in band power
`Ž
`during task performance as compared to a reference or resting period cf.
`.
`Fig. 7 .
`
`.
`Ž
`2. Theta frequency as measured by TF covaries with
`w
`x.
`Ž
`alpha frequency as measured by IAF 34,76 .
`3. Theta and alpha band power are related to each other,
`Ž
`although in a reciprocal or ‘opposite’ way see Sections
`.
`2–4 below and Table 2 ,
`theta clearly is an
`4. Animal research has shown that
`oscillatory component of the hippocampal EEG which
`Ž
`is related to memory processes see e.g., the review by
`w
`x
`.
`Miller 107 and Section 5 below ,
`5. Research from our laboratory indicates that theta band
`power increases in response to memory demands just as
`Ž
`hippocampal theta in animals does
`see Sections 4.3
`.
`and 5 below .
`Thus, the concepts of desynchronization and synchro-
`Ž
`.
`nization will also be used for the individually defined
`theta band. In a similar way, these concepts will be applied
`to the different subbands of alpha. Visual inspection of
`‘alpha waves’ in the EEG may invite the misleading
`interpretation that there is only a single rhythm which may
`just vary in frequency. As the results from factor analyses
`have shown, there are at least two independent components
`of alpha activity which must be distinguished. Based on
`physiological and experimental evidence, many authors
`meanwhile assume that there is an entire population of
`w
`x.
`Ž
`different alpha rhythms e.g., Refs. 6,166,167 . Thus, it
`seems quite obvious to assume that during desynchroniza-
`tion different alpha rhythms in different subbands start to
`Ž
`oscillate with different frequencies
`for more details see
`.
`Section 5 .
`
`2. Tonic changes and differences in the alpha and theta
`frequency range
`
`The type of EEG changes or differences which are
`discussed in the following sections my be termed ‘tonic’ in
`Ž
`order to contrast them from ‘phasic’ changes. Phasic or
`.
`event-related changes in the EEG are more or less under
`volitional control and occur at a rapid rate, whereas tonic
`Ž
`.
`changes are not or less under volitional control and occur
`at a much slower rate. Phasic changes in the EEG are task
`andror stimulus related. Tonic changes, on the other hand,
`
`

`

`W. KlimeschrBrain Research ReÕiews 29 1999 169–195
`)
`(
`
`175
`
`occur over the life cycle and in response to circadian
`rhythms, fatigue, distress, neurological disorders, etc.
`
`2.1. Age related changes and performance related differ-
`ences i