Quantitative EEG in early Alzheimer's disease patients - power spectrum and complexity features.
ABSTRACT The goal of this study was to investigate the EEG signs of early stage Alzheimer's disease (AD) by conventional analyses and by methods quantifying linear and nonlinear EEG-complexity. The EEG was recorded in 12 mild AD patients and in an age-matched healthy control group (24 subjects) in both eyes open and eyes closed conditions. Frequency spectra, Omega-complexity and Synchronization likelihood were calculated on the data. In the patients a significant decrease of the relative alpha and increase of the theta power were found. Remarkably increased Omega-complexity and lower Synchronization likelihood were observed in AD in the 0.5-25 Hz frequency ranges. It is concluded that both spectral- and EEG-complexity changes can be found already in the early stage of AD in a wide frequency range. Application of conventional EEG analysis methods in combination with quantification of EEG-complexity may improve the chances of early diagnosis of AD.
- SourceAvailable from: Yuriem Fernandez Garcia[Show abstract] [Hide abstract]
ABSTRACT: To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.International journal of Alzheimer's disease. 01/2014; 2014:180741.
- [Show abstract] [Hide abstract]
ABSTRACT: Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.Frontiers in Aging Neuroscience 01/2014; 6:55. · 5.20 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.International journal of Alzheimer's disease. 01/2014; 2014:349249.
Fast track report
Quantitative EEG in early Alzheimer's disease patients — Power spectrum
and complexity features
Balázs Cziglera,⁎, Dóra Csikósa, Zoltán Hidasib, Zsófia Anna Gaála, Éva Csibrib,
Éva Kissc, Pál Salaczb, Márk Molnára
aInstitute for Psychology, Hungarian Academy of Sciences, Budapest, Hungary
bDepartment of Psychiatry, Semmelweis University Medical School, Budapest, Hungary
cNational Institute of Psychiatry and Neurology, Budapest, Hungary
Received 3 October 2007; received in revised form 13 November 2007; accepted 14 November 2007
Available online 22 November 2007
The goal of this study was to investigate the EEG signs of early stage Alzheimer's disease (AD) by conventional analyses and by methods
quantifying linear and nonlinear EEG-complexity. The EEG was recorded in 12 mild AD patients and in an age-matched healthy control
group (24 subjects) in both eyes open and eyes closed conditions. Frequency spectra, Omega-complexity and Synchronization likelihood were
calculated on the data. In the patients a significant decrease of the relative alpha and increase of the theta power were found. Remarkably
increased Omega-complexity and lower Synchronization likelihood were observed in AD in the 0.5–25 Hz frequency ranges. It is concluded
that both spectral- and EEG-complexity changes can be found already in the early stage of AD in a wide frequency range. Application
of conventional EEG analysis methods in combination with quantification of EEG-complexity may improve the chances of early diagnosis
© 2007 Elsevier B.V. All rights reserved.
Keywords: Alzheimer's disease; Quantitative EEG; Nonlinear dynamics; Omega-complexity; Synchronization likelihood
7.6 million people were reported suffering from dementia
in Europe in 2000 (Blennow et al., 2006) which makes it one
of the most frequent diseases amongst the elderly. Dementia
is caused by Alzheimer's disease (AD) in 40–60% of the
cases, therefore it is the most prevalent of neurodegenerative
diseases (Alloul et al., 1998). These data underscore the im-
portance of early diagnosis and the need to develop ap-
propriate methods for monitoring therapy and the course of
Different phases of the disease can be defined based on the
progressive development of neurofibrillary tangles and amyloid
plaques (Braak and Braak, 1995; Braak and Del Tredici, 2004).
Impairment of the long corticocortical tracts (Wurtman et al.,
1996) and synaptic degeneration (Vellas and Fitten, 2000) were
The rate of correctly identified AD-cases by electrophysio-
logical methods varies within a wide range, between 29% and
42% in the early and between 60% and 80% in the later stage
(Stam et al., 1996). The most frequently observed spectral
change is the “slowing” of the EEG, which supposedly cor-
relates with the degree of cognitive decline (Bennys et al.,
2001). Increase of theta and decrease of beta power is expected
to appear in the early stages, followed by a decrease of alpha
frequency (Robinson et al., 1994). In severely demented AD
patients the decrease of alpha activity is accompanied by an
increase of delta power (Coben et al., 1985; Dringenberg,
Available online at www.sciencedirect.com
International Journal of Psychophysiology 68 (2008) 75–80
⁎Corresponding author. Institute for Psychology of the Hungarian Academy
of Sciences, Budapest POB 398, H-1394 Hungary. Tel.: +36 1 354 2290;
fax: +36 1 354 2416.
E-mail address: email@example.com (B. Czigler).
0167-8760/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
The study of complex systems such as the brain, showing
both linear and nonlinear features requires appropriate techni-
ques for the analysis of its functional properties, made possible
only by recent advances in the mathematics of nonlinear
dynamical systems (Stam, 2005). It is to be expected that since
interneuronal connections are impaired in AD these changes can
be revealed by “complexity analyses” of the EEG (Pijnenburg
et al., 2004). In this context, “dynamical complexity” is related
to the level of interactions (synchrony) between different parts
of a functional network (Stam, 2005). These methods have
provided useful information in AD (for reviews, see Jeong,
2004; Stam 2005). Most of the studies in this field were con-
cerned with global characteristics of the EEG and did not
investigate possible changes of individual frequency bands
(from theta to gamma), although their different functional roles
have become well known (Klimesch, 1999; Klimesch et al.,
2007 and others).
The aim of the present study was to investigate linear and
nonlinear EEG-complexity in various EEG-frequency bands to
fast and slow EEG-frequencies correspond to various cognitive
processes, depending on the efficiency of short-, and long-term
connectivities (Buzsaki and Draguhn, 2004). Thus, it seemed
reasonable to hypothesize that already in the early AD patients
various frequency bands corresponding to different aspects of
12 mild AD patients (10 females, mean age 72.34 years, SD:
5.9 years) were studied. The diagnosis of probable Alzheimer's
disease was based on the criteria of NINCDS–ADRDA
(McKhann et al., 1984). All patients were subjected to internal
medical, neurological and psychiatric examination and were
evaluated on diagnostic (Hachinski et al., 1974; Hamilton 1960)
CTor MR scans.
Inclusion criteria for the AD group were: probable AD by the
criteria mentioned above, MMSE score between 20 and 24, age
above 50 years, unaltered medication in the previous 3 months.
Exclusion criteria were: cause of dementia other than AD ac-
cording to CT, MRI or laboratory findings, clinical signs of
vascular dementia or Hachinski score above 4, severe depres-
sion (Hamilton Depression score beyond 15). Patients with
other neurological diseases, impairment of hearing, taking
medication that influences the EEG (e.g. neuroleptic drugs,
benzodiazepines, cholinesterase inhibitors) were also excluded
from the study. The patients were directed to our laboratory
from the Semmelweis University's Teaching Hospital of
Psychiatry and Psychotherapy, Budapest, and from the National
Institute of Psychiatry and Neurology, Budapest.
The control group consisted of 24 healthy elderly subjects
(16 females, MMSE 29–30, mean age 72.5 years, SD:
5.62 years), who were compensated for their participation. The
study was allowed by the Ethics Committee of the Institute of
Psychology of the Hungarian Academy of Sciences and also by
that of the Semmelweis University's Teaching Hospital of
Psychiatry and Psychotherapy.Allsubjectsgave their consent in
the study in writing.
2.2. Data acquisition
2 min EEG-recordings were performed both in “eyes
closed” and “eyes open” conditions, (21 electrodes positioned
according to the international 10–20 system, nose as reference,
linked earlobes as ground). Vertical and horizontal eye move-
ments were recorded by electrodes placed above and beneath
the right eye, and in the lateral canthi. NuAmps system was
used for recording (A/D frequency: 1000 Hz, bandpass filter:
0.5–45 Hz, 10% Cosine window, FIR). The EEG was seg-
mented into 2048 ms epochs. Sweeps exceeding +/−70 µV
were excluded by automatic artifact rejection, which was
followed by visual artifact screening. In the case of two control
subjects andone patient the total lengthof the artifact-free EEG
did not exceed 30 s and so these subjects were omitted from
the analyses. The average number of artifact-free epochs per
person was 28.4.
2.3. Data analyses
Spectral and complexity analyses were performed in the
following frequency bands: delta: 0.5–4 Hz, theta: 4–8 Hz,
alpha1: 8–11 Hz, alpha2: 11–14 Hz, beta1: 14–25 Hz, beta2:
25–35 Hz. Calculation of relative spectra for the frequency
ranges described above was performed by Neuroscan 4.3 and
MATLAB 7.1 softwares. Statistical analyses were carried out
for the whole scalp (as the average of the spectral values
measured on all electrodes), for the anterior (Fp1, Fp2, F3,
F4, F7, F8, Fz) and posterior (P3, P4, O1, O2, T5, T6, Pz)
areas, for the right side (Fp2, F4, C4, P4, O2, F8, T4, T6) and
for the left side (Fp1, F3, C3, P3, O1, F7, T3, T5). Three-
way ANOVAs were calculated in Group (AD vs. control)×
Condition (eyes open vs. closed)×Localization (anterior vs.
posterior) and in Group×Condition×Side (right vs. left)
For complexity analyses the DigEEG software was used for
computing Synchronization likelihood and Omega-complexity
on the same data (C.J. Stam, VU University Medical Center,
Amsterdam, by permission). A detailed description of these
methods can be found in papers by Stam and van Dijk (2002)
and Wackermann (1999). The basic principles of the methods
are summarized below.
Synchronization likelihood is based on comparison of
vectors representing the state of the system reconstructed in
state space by time-delay embedding. It is a measure of the
linear and nonlinear interdependency (strength of coupling)
between a time series and one or more other time series (e.g.
EEG-channels). In case of total synchrony the value of
Synchronization likelihood is 1 while for completely indepen-
dent systems it equals 0.
76 B. Czigler et al. / International Journal of Psychophysiology 68 (2008) 75–80
Omega-complexity is obtained by spatial principle compo-
nent analysis on the EEG-channel data where the entropy of the
normalized eigenvalues of the components is defined as the
logarithm of Omega. If one component can describe all the time
series, it means that a single source (“mechanism”) would be
responsible for the dynamics of the EEG. Low and high Omega
values correspond to high and low levels of synchronization,
For the data shown in the present study Synchronization
likelihood and Omega-complexity were calculated for the
whole scalp, taking all electrodes into account.EEG-complexity
parameters were analyzed by computing Group×Condition
analyses of variance.
3.1. Frequency spectral analysis
In the theta band significant between-group differences were
observed. Relative theta power was higher in the AD group in
both eyes closed F(1,29)1=24.81; pb0.001) and eyes open
(F=6.21; pb0.02) conditions for the whole scalp (means of all
electrodes); for the left side in eyes closed (F=21.06, pb0.001)
and eyes open (F=5.63; pb0.03) conditions, for the right side
in eyes closed (F=18.14; pb0.001) and eyes open (F=9.85;
pb0.01) conditions, for the anterior (eyes closed: F=6.32;
pb0.02; eyes open: F=4.8; pb0.05) and the posterior (eyes
closed: F=11.62; pb0.001; eyes open: F=7.56; pb0.01)
Significant and marginally significant differences were also
revealed in the alpha2 band, where a decrease in relative power
was seen in the AD group compared to the control group for
the whole scalp (eyes closed: F=5.1; pb0.05 and eyes open:
F=4.1; pb0.05), for the left side (eyes closed: F=6.14;
pb0.02 and eyes open: F=4.81; pb0.05), for the right side
(eyes closed: F=4.13; pb0.05 and eyes open: F=3.3;
pb0.08), for the anterior (eyes closed: F=4.52; pb0.05; not
significant in the eyes open condition), and for the posterior
(eyes closed: F=3.2; pb0.09 and eyes open: F=4.13; pb0.05)
regions. Group averages of alpha2 and theta bands are shown in
In both groups opening the eyes resulted in a significant
decrease in the alpha1 (F=32.141; pb0.0001) and an increase
in the beta1 (F=22.091; pb0.0001) and beta2 (F=13.091;
pb0.001) bands. No significant Group×Condition (eyes closed
vs. open) interaction was obtained.
3.2. Complexity analyses
Synchronization likelihood was significantly lower in AD in
all frequency bands in both eyes closed and eyes open con-
ditions except for the beta2 band where this difference was not
significant in either condition. The significant Synchronization
likelihood differences found in the various frequency bands and
recording conditions were the following: delta (eyes closed:
F=4.68; pb0.05 and eyes open: F=6.75; pb0.02); theta (eyes
closed: F=6.75; pb0.02 and eyes open: F=11.53; pb0.01);
alpha1 (eyes open: F=8.4; pb0.01); alpha2 (eyes closed
condition F=5.48; pb0.05); beta1 band (eyes closed condition
F=4.3; pb0.05). A marginally significant difference was also
found in the alpha1 band in the eyes closed condition (F=3.32;
pb0.08), (see Fig. 2 for group averages in each frequency band
No significant Group×Condition interaction or Condition
main effect was observed for Synchronization likelihood.
Omega-complexity was significantly higher in the AD
group for the delta band in the eyes open (F=4.43; pb0.05),
and for the theta band in both conditions (eyes closed:
F=4.73; pb0.05 and eyes open: F=6.86; pb0.02), for the
alpha1 band in the eyes open condition (F=6.9; pb0.02), for
the alpha2 band in both conditions (F=7.18; pb0.02 and
F=6.34; pb0.02, respectively), and for the beta1 range for
the eyes closed condition (F=8.42; pb0.01). A marginally
significant difference was found in the eyes closed condition
for the delta frequency range (F=3.52; pb0.07) and the
alpha1 range (F=3.67; pb0.07). Group averages for each
band and condition are shown in Fig. 2. As a result of open-
ing the eyes Omega increased considerably in both groups
1If not stated otherwise the degrees of freedom were (1,29).
Fig. 1. Relative power (%) of theta and alpha2 bands in eyes open and eyes
closed conditions in the AD and control groups. Vertical bars denote 95%
77 B. Czigler et al. / International Journal of Psychophysiology 68 (2008) 75–80
(F=9.6096; pb.005). No significant Group×Condition effect
Our findings support earlier data showing spectral “slowing”
of EEG in AD (Coben et al., 1985; Brenner, 1999; van der Hiele
projecting to the hippocampus and the neocortex may play an
important role in this process (Dringenberg, 2000) It is to be
noted, however, that the malfunction of other neurotransmitter
systems such as the monoaminergic systems is also involved in
AD (Wong et al., 2006).
According to our findings the amount of relative theta band
increased and that of the fast alpha range decreased in AD. This
effect could be seen both in the eyes closed and eyes open
conditions, in both hemispheres and also for both the anterior
and posterior areas. It is to be emphasized that in the present
study these changes were detected in the very early stage of
Former studies foundlower dimensional complexityinADby
the correlation dimension method (Jeong, 2004) and also by
calculating the Lempel–Ziv complexity at a few electrode sites
(Abasolo etal.,2006).Inthese formerstudies differentfrequency
bands were not analyzed separately. More recently, a decrease of
synchronization in the beta frequency band was observed by the
Synchronization likelihood was lower in the patient group in all
frequency bands both in eyes closed and eyes open conditions
except for the fastest (beta2) frequency band. The increase of
Omega-complexity and the decrease of Synchronization like-
lihood correspond to an increased number of independent neural
Fig. 2. Synchronization likelihood and Omega-complexity in the two groups in the six frequency bands, in eyes open and eyes closed conditions (averages).
78 B. Czigler et al. / International Journal of Psychophysiology 68 (2008) 75–80
assemblies and thus probably indicate the disintegration of
different parts of the nervous system, including different regions
of the neocortex. This is to be expected, given the neuropatho-
logical changes characterizing AD, which compromise the
of the brain, indispensable for cognitive activity. The reduced
coherence found in AD in the alpha and beta bands (Locatelli
et al., 1998; Jeong et al., 2001; Adler et al., 2003; Hogan et al.,
complexity-changes in the beta2 band may indicate that very
stage of the disease.
While the spectral measures showed changes only in the theta
andalpha2 bands in the AD group,results of the EEG-complexity
analyses verified decreased synchronization in all frequency
“classical” gamma band, in which lower synchronization was
found in AD by Stam and van Dijk (2002), based on the present
that consistent changes indicative of lower degree of synchroniza-
tion were observed in all other frequency bands suggest that
already in early AD the capacity for both short-, and long-range
connectivity are reduced. Such reduction may provide the basis
for the compromise of various types of cognitive processes
(like attention, memory, etc.). Our finding, that a wide range of
frequency bands are involved in AD supports that of Koenig et al.
(2005)whoapplieda new method(GlobalField Synchronization)
nonlinear methods were applied in the analyses.
Although the number of subjects in the present study is rela-
tivelylow,the statisticallysignificant findings mayserve as a basis
for further studies in which the severity of AD is correlated with
spectral and complexity analyses. According to the present ob-
servations, both Omega-complexity and Synchronization like-
with Alzheimer's disease complementing spectral measures. It
remains to be seen if the results provided by these methods will
prove to be sensitive indicators of the progression of pathological
processes such as those seen in AD and, correspondingly, if these
will help in the quantitative assessment of therapy as well.
We thank Erzsébet Kaldenecker for her assistance in data
acquisition. The authors gratefully acknowledge the contribution
of J. C. Stam in providing the mathematical formulas outlined
in the Appendix. Grant support: OTKA Hungarian Research
Appendix A. Calculation of the Synchronization likelihood
(Stam and van Dijk, 2002)
The Synchronization likelihood (SL) is a measure of general-
systems X and Y. The first step in the computation of the SL is to
convert the time series xiand yirecorded from X and Yas a series
of state space vectors using the method of time-delay embedding
where L is the time lag, and m the embedding dimension. From a
time series ofN samples,N−(m×L) vectorscan bereconstructed.
State space vectors Yiare reconstructed in the same way.
SL is defined as the conditional likelihood that the distance
between Yiand Yjwill be smaller than a cut-off distance ry,
given that the distance between Xiand Xjis smaller than a cut-
off distance rx. In the case of maximal synchronization this
likelihood is 1; in the case of independent systems, it is a small,
but nonzero number, namely Pref. This number is the likelihood
that two randomly chosen vectors Y (or X) will be closer than
the cut-off distance r. In practice, the cut-off distance is chosen
such that the likelihood of random vectors being close is fixed at
Pref, which is chosen identically for X and for Y. To follow how
Prefis used to fix rxand rythe correlation integral is considered:
Xi¼ xi;xiþL;xiþ2?L;xiþ3?LN ; xiþ m?1
N N ? w
The correlation integral Cris the likelihood that two random-
ly chosen vectors X will be closer than r. The vertical bars
represent the Euclidean distance between the vectors. N is the
(Theiler, 1986) and θ is the Heaviside function: θ(X)=0 if
XN=0 and θ(X)=1 if Xb0. Rxis chosen so that Crx=Prefand ry
is chosen so that Cry=Pref. The SL between X and Y can be
formally defined as:
h r ?jXi? Xjj
N N ? w
h rx?jXi? Xjj
h ry?jYi? Yjj
SL is a symmetric measure of the strength of synchronization
between X and Y (SLXY=SLYX).
Calculation of Omega-complexity
Omega-complexity characterizes the covariance-relations of
multichannel EEG data (Wackermann, 1999). After computing
the covariance-values between the electrode channels, a principal
component analysisis implemented to explore ifthere are mutual
factors (components) that can describe the variance of the vari-
ables. The eigenvalue of the components expresses the extent
to which the component is responsible for the variance of
the original variables. The Shannon-entropy of the normalized
eigenvalues equals the logarithm of Omega:
logX ¼ ?
where λiis the normalized eigenvalue of the i-th component.
The formula reveals that if one component can describe all
the time series, it means that a single source (“mechanism”)
would be responsible for the dynamics of the EEG, in which
79B. Czigler et al. / International Journal of Psychophysiology 68 (2008) 75–80
case logΩ=0 and thus Omega equals 1. If independent signals
are recorded in the channels (e.g. white-noise), then Omega
equals the number of channels. A low Omega value implies a
more simple, and a high Omega a more complex signal.
Abasolo, D., Hornero, R., Gomez, C., Garcia, M., Lopez, M., 2006. Analysis of
EEG background activity in Alzheimer's disease patients with Lempel–Ziv
complexity andcentraltendency measure.Medical Engineering & Physics28,
Adler, G., Brassen, S., Jajcevic, A., 2003. EEG coherence in Alzheimer's
dementia. Journal of Neural Transmission 110, 1051–1058.
Alloul, K., Sauriol, L., Kennedy, W., Laurier, C., Tessier, G., Novosel, S.,
Contandriopoulos, A., 1998. Alzheimer's disease: a review of the disease, its
Bennys, K., Rondouin, G., Vergnes, C., Touchon, J., 2001. Diagnostic value of
quantitative EEG in Alzheimer's disease. Neurophysiologie Clinique-Clinical
Neurophysiology 31, 153–160.
Blennow, K., de Leon, M.J., Zetterberg, H., 2006. Alzheimer's disease. Lancet
Braak, H., Braak, E., 1995. Staging of Alzheimer's disease-related neurofi-
brillary changes. Neurobiology of Aging 16, 271–278.
Braak, H., Del Tredici, K., 2004. Alzheimer's disease: intraneuronal alterations
precede insoluble amyloid-beta formation. Neurobiology of Aging 25,
Brenner, R., 1999. EEG and Dementia, In: Niedermeyer, E., Lopes da Silva, F.
(Eds.), Electroencephalography: Basic Principles, Clinical Applications and
Related Fields, 4th ed. Williams & Wilkins, Baltimore, pp. 349–359.
Buzsaki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks.
Science 304, 1926–1929.
Coben, L.A., Danziger, W., Storandt, M., 1985. A longitudinal EEG study of
Electroencephalography and Clinical Neurophysiology 61, 101–112.
evidence that cholinergic–monoaminergic interactions contribute to EEG
slowing and dementia. Behavioural Brain Research 115, 235–249.
Archives of General Psychiatry 40, 812.
Hachinski, V.C., Lassen, N.A., Marshall, J., 1974. Multi-infarct dementia. A
cause of mental deterioration in the elderly. Lancet 2, 207–210.
and Psychiatry 23, 56–62.
Hogan, M.J., Swanwick, G.R.J., Kaiser, J., Rowan, M., Lawlor, B., 2003.
Memory-related EEG power and coherence reductions in mild Alzheimer's
disease. International Journal of Psychophysiology 49, 147–163.
Jeong, J., Gore, J.C., Peterson, B.S., 2001. Mutual information analysis of the
EEG in patients with Alzheimer's disease. Clinical Neurophysiology 112,
Jeong, J.S., 2004. EEG dynamics in patients with Alzheimer's disease. Clinical
Neurophysiology 115, 1490–1505.
Klimesch, W., 1999. EEG alpha and theta oscillations reflect cognitive and
memory performance: a review and analysis. Brain ResearchBrain Research
Reviews 29, 169–195.
Klimesch, W., Sauseng, P., Hanslmayr, S., 2007. EEG alpha oscillations: the
inhibition-timing hypothesis. Brain Research Reviews 53, 63–88.
Koenig, T., Prichep, L., Dierks, T., Hubl, D., Wahlund, L.O., John, E.R., Jelic,
V., 2005. Decreased EEG synchronization in Alzheimer's disease and mild
cognitive impairment. Neurobiology of Aging 26, 165–171.
Locatelli, T., Cursi, M., Liberati, D., Franceschi, M., Comi, G., 1998. EEG
coherence in Alzheimer's disease. Electroencephalography and Clinical
Neurophysiology 106, 229–237.
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M.,
1984. Clinical diagnosis of Alzheimer's disease: report of the NINCDS–
ADRDAWorkGroup under theauspices of Department of HealthandHuman
Services Task Force on Alzheimer's Disease. Neurology 34, 939–944.
Pijnenburg, Y.A.L., Made, Y.V., van Walsum, A.M.V., Knol, D.L., Scheltens, P.,
and Alzheimer's disease during a working memory task. Clinical Neurophy-
siology 115, 1332–1339.
Robinson, D.J., Merskey, H.,Blume, W.T., Fry, R., Williamson,P.C.,Hachinski,
disease. Archives of Neurology 51, 280–284.
Rulkov, N.F., Sushchik, M.M., Ysimring, L.S., Abarbanel, H.D.I., 1995.
Generalized synchronization of chaos in directionally coupled chaotic
systems. Physical Review E 51, 980–994.
Stam, C.J., 2005. Nonlinear dynamical analysis of EEG and MEG: review of an
emerging field. Clinical Neurophysiology 116, 2266–2301.
Stam, C.J., Jelles, B., Achtereekte, H.A.M., vanBirgelen, J.H., Slaets, J.P.J.,
1996. Diagnostic usefulness of linear and nonlinear quantitative EEG
of generalized synchronization in multivariate data sets. Physica D-Nonlinear
Phenomena 163, 236–251.
Takens, F., 1981. Detecting strange attractors in turbulence. Lecture Notes in
Mathematics 898, 366–381.
Theiler, J., 1986. Spurious dimension from correlation algorithms applied to
limited time-series data. Physical Review A 34, 2427–2432.
van der Hiele, Vein, A.A., van der, W.A., van der, G.J., Westendorp, R.G.,
Bollen, E.L., van Buchem, M.A., van Dijk, J.G., Middelkoop, H.A., 2007.
EEG and MRI correlates of mild cognitive impairment and Alzheimer's
disease. Neurobiology of Aging 28, 1322–1329.
Vellas, B., Fitten, L., 2000. Research and Practice in Alzheimer's Disease.
Springer, New York.
Wackermann, J., 1999. Towards a quantitative characterisation of functional
states of the brain: from the non-linear methodology to the global linear
description. International Journal of Psychophysiology 34, 65–80.
Molecular, Cellular and Medical Aspects, 7th ed. Elsevier, pp. 781–790.
Wurtman, R., Corkin, S., Growdon, J., Nitsch, R., 1996. The Neurobiology of
Alzheimer's Disease. The New York Academy of Sciences, New York.
80B. Czigler et al. / International Journal of Psychophysiology 68 (2008) 75–80