Movement related activity in the high gamma range of the human EEG.
ABSTRACT Electrocorticographic (ECoG) recordings obtained using intracranially implanted electrodes in epilepsy patients indicate that high gamma band (HGB) activity of sensorimotor cortex is focally increased during voluntary movement. These movement related HGB modulations may play an important role in sensorimotor cortex function. It is however currently not clear to what extent this type of neural activity can be detected using non-invasive electroencephalography (EEG) and how similar HGB responses in healthy human subjects are to those observed in epilepsy patients. Using the same arm reaching task, we have investigated spectral power changes both in intracranial ECoG recordings in epilepsy patients and in non-invasive EEG recordings optimized for detecting HGB activity in healthy subjects. Our results show a common HGB response pattern both in ECoG and EEG recorded above the sensorimotor cortex contralateral to the side of arm movement. In both cases, HGB activity increased around movement onset in the 60-90 Hz range and became most pronounced at reaching movement end. Additionally, we found EEG HGB activity above the frontal midline possibly generated by the anterior supplementary motor area (SMA), a region that was however not covered by the intracranial electrodes used in the present study. In summary, our findings show that HGB activity from human sensorimotor cortex can be non-invasively detected in healthy subjects using EEG, opening a new perspective for investigating the role of high gamma range neuronal activity both in function and dysfunction of the human cortical sensorimotor network.
- SourceAvailable from: Ran Xiao[Show abstract] [Hide abstract]
ABSTRACT: Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.PLoS ONE 01/2014; 9(1):e85192. · 3.53 Impact Factor
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ABSTRACT: High frequency oscillations (HFOs) are emerging as biomarkers of epileptogenicity. They have been shown to originate from small brain regions. Surprisingly, spontaneous HFOs can be recorded from the scalp. To understand how is it possible to observe these small events on the scalp, one avenue is the analysis of the cortical correlates at the time of scalp HFOs. Using simultaneous scalp and intracranial recordings of 11 patients, we studied the spatial distribution of scalp events on the cortical surface. For typical interictal epileptiform discharges the subdural distributions were, as expected, spatially extended. On the contrary, for scalp HFOs the subdural maps corresponded to focal sources, consisting of one or a few small spatial extent activations. These topographies suggest that small cortical areas generated the HFOs seen on the scalp. Similar scalp distributions corresponded to distinct distributions on a standard 1 cm subdural grid and averaging similar scalp HFOs resulted in focal subdural maps. The assumption that a subdural grid "sees" everything that contributes to the potential of nearby scalp contacts was not valid for HFOs. The results suggest that these small extent events are spatially undersampled with standard scalp and grid inter-electrode distances. High-density scalp electrode distributions seem necessary to obtain a solid sampling of HFOs on the scalp. A better understanding of the influence of spatial sampling on the observation of high frequency brain activity on the scalp is important for their clinical use as biomarkers of epilepsy.Brain Topography 10/2013; · 3.67 Impact Factor
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ABSTRACT: For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations' functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal.PLoS ONE 01/2014; 9(2):e87507. · 3.53 Impact Factor
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Movement related activity in the high gamma range of the
Tonio Ball,a,b,f,⁎,1Evariste Demandt,c,1Isabella Mutschler,a,d,eEva Neitzel,b
Carsten Mehring,b,cKlaus Vogt,cAd Aertsen,b,fand Andreas Schulze-Bonhagea,b
aEpilepsy Center, University Clinics, Albert-Ludwigs-University, Freiburg, Germany
bBernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, Germany
cNeurobiology and Animal Physiology, Faculty of Biology I, Albert-Ludwigs-University, Freiburg, Germany
dDepartment of Psychiatry, University of Basel, Basel, Switzerland
eDepartment of Psychology, University of Basel, Basel, Switzerland
fNeurobiology and Biophysics, Faculty of Biology III, Albert-Ludwigs-University, Freiburg, Germany
Received 4 December 2007; revised 15 February 2008; accepted 19 February 2008
Available online 4 March 2008
Electrocorticographic (ECoG) recordings obtained using intracra-
nially implanted electrodes in epilepsy patients indicate that high
gamma band (HGB) activity of sensorimotor cortex is focally increased
during voluntary movement. These movement related HGB modula-
tions may play an important role in sensorimotor cortex function. It is
however currently not clear to what extent this type of neural activity
can be detected using non-invasive electroencephalography (EEG) and
how similar HGB responses in healthy human subjects are to those
observed in epilepsy patients. Using the same arm reaching task, we
have investigated spectral power changes both in intracranial ECoG
recordings in epilepsy patients and in non-invasive EEG recordings
optimized for detecting HGB activity in healthy subjects. Our results
show a common HGB response pattern both in ECoG and EEG
recorded above the sensorimotor cortex contralateral to the side of arm
movement. In both cases, HGB activity increased around movement
onset in the 60–90 Hz range and became most pronounced at reaching
movement end. Additionally, we found EEG HGB activity above the
frontal midline possibly generated by the anterior supplementary
motor area (SMA), a region that was however not covered by the
intracranial electrodes used in the present study. In summary, our
findings show that HGB activity from human sensorimotor cortex can
be non-invasively detected in healthy subjects using EEG, opening a
new perspective for investigating the role of high gamma range
neuronal activity both in function and dysfunction of the human
cortical sensorimotor network.
© 2008 Elsevier Inc. All rights reserved.
voluntary movement (Salenius and Hari, 2003; Schnitzler et al.,
investigating oscillatory brain activity is the electroencephalogram
(EEG) recorded from the scalp surface. Movement related changes
in the alpha and beta band power of the EEG have been repeatedly
described (Neuper and Pfurtscheller, 2001). However, only few
cases of movement related modulation of gamma frequency range
power in the human EEG have been reported (Pfurtscheller et al.,
1993; Pfurtscheller and Neuper, 1992). In these studies, a narrow
frequency band around 40 Hz was investigated, showing enhanced
spectral power prior to movement onset and suppression during
Movement related oscillations in the 40 Hz range were proposed
to be involved infunctional interactions between sensorimotor areas
during movement preparation (Pfurtscheller et al., 1993), to reflect
increased cortical excitability (Aoki et al., 1999), to play a role in
focused attention (Bouyer et al., 1987), sensorimotor integration
(Aoki et al., 1999; Szurhaj et al., 2005), or in the neuronal
computation of details of movement execution (for a discussion of
these hypotheses see Rickert et al., 2005).
Contrasting the ‘40 Hz activity’ described in non-invasively
recorded EEG, invasive recordings in epilepsy patients have
provided a different picture of movement related gamma oscilla-
tions: in particular in the pre- and postcentral gyrus, gamma band
responses were found in a highly reproducible way both during
externally paced (Crone et al., 1998) and self-paced hand and arm
movements (Ball et al., 2004; Miller et al., 2007; Ohara et al., 2000;
Pfurtscheller et al., 2003; Szurhaj et al., 2005). Moreover, all these
studies agree that gamma activity was most pronounced during
movement execution, instead of during movement preparation.
NeuroImage 41 (2008) 302–310
⁎Corresponding author. Epilepsy Center, University Clinics Freiburg,
Hugstetter Str. 49, 79095 Freiburg, Germany.
E-mail address: email@example.com (T. Ball).
1Equally contributing authors.
Available online on ScienceDirect (www.sciencedirect.com).
1053-8119/$ - see front matter © 2008 Elsevier Inc. All rights reserved.
Author's personal copy
bands2were typically found to show increased spectral power
during movement execution.
(Crone et al., 2006). Incentives for this new interest were results
indicating that HGB is closely correlated to cortical function
level dependent (BOLD) signal as measured with functional
et al., 2001; Mukamel et al., 2005; Niessing et al., 2005). The
assumption that HGB activity has functional importance has been
further strengthened by evidence from invasive recordings in
epilepsy patients, indicating that cross-frequency coupling between
theta (4–8 Hz) and HGB activity is involved in the functional
coordination of cortical areas in humans (Canolty et al., 2006). The
exact role of movement related HGB activity, however, remained
elusive. To further investigate this type of brain activity, a non-
invasive approach would be highly useful. Thus, the aim of the
present study was to re-investigate movement related brain activity
in the high gamma band of the EEG of healthy human subjects. To
obtain information about the brain regions, time periods, and
frequencies where we could expect HGB modulation, we also
analyzed electrocorticographic (ECoG) recordings that we obtained
in epilepsy patients during the same motor task as used in the EEG
Eight subjects (4 females, 4 males, mean age=24 years,
range=20–29 years) participated in this study after giving their
written informed consent. All participants were without a history of
with a similar experimental setup. Subjects were right-handed
according to the Edinburgh handedness questionnaire (Oldfield,
1971): mean=89%, range=70–100%.
EEG experiments were carried out in an electrically shielded,
subjects’ arms and shoulders from their field of view to avoid visual
was peripherally supported assuring that the moving arm of the
subjects did not touch the cape. In front of the subjects, five buttons
(button diameter: 40 mm) were mounted on a table. The buttons
were horizontally arranged in a cross with one central and four
peripheral positions. Relative to the central position, the peripheral
positions were to the right, left, front, and back. Center-to-center
distances between the central and peripheral buttons were 20 cm. A
minimal force of 0.06 N had to be applied to push each button.
The recording session was divided in 7 to 11 runs of approx.
10 min duration each. As starting position for each run, the subjects
were instructed to rest their hand on the center button and their arm
on the supporting table. Due to the low force level required to push
the buttons, no active muscle contraction was required to keep the
central button depressed, hence, the weight of the hand and arm
was sufficient. Subjects were instructed to relax their arm in this
position and received feedback from the experimenter if their arm
was not properly relaxed (as judged from EMG recordings, see
below). Subjects were instructed to perform center-out and center-
in arm reaching movements self-paced approximately every 4 to
10 s. Each time when either one of the peripheral targets or the
central target was reached after a center-out or a center-in move-
ment, respectively, subjects waited for approximately 4 to 10 s in a
relaxed position similar to the starting position described above,
before they initiated the next (center-in or center-out) movement.
Movement direction of the center-out movements was trial-by-trial
self-chosen by the subjects. Triggers generated by releasing and
pushing the buttons were used to determine movement onset and
movement end, respectively.
Using a 64-channel EEG system (SynAmps, NeuroScan, El
recorded from 58 standard scalp positions (EASYCAP, Herrsching-
Breitbrunn, Germany) equally distributed over both hemispheres.
All channels were recorded against a reference electrode at the
vertex (CZ). The ground electrode was positioned in the right
occipital region. Amplification was 12,500×, sampling frequency
was at 2500 Hz. At these settings and given the digital resolution of
16 bit of the amplifier system used, the dynamic range for the scalp
EEG channels was 400 µV, i.e. 0.006 µV/bit. Electrode impedances
were kept below 5 kΩ. The EMG above the right superficial flexor
digitorum longus muscle (pars indicis) and of the right deltoid were
recorded in all subjects. The EOG was also recorded to reject trials
contaminated with eye movements from further analysis. Amplifi-
cation of EMG and EOG channels was 2500×. If the dynamic range
at this amplification (i.e. 2.2 mV) was too small in respect to EMG
amplitude, weused 1000×amplification (dynamic range of5.5mV).
Two patients (P1: female, aged 55 and P2: male, aged 20)
suffering from intractable pharmaco-resistant epilepsy with a focal
cortical dysplasia in the left fronto-polar cortex were included in the
study after having given their informed consent. The patients were
right-handed after a modified Oldfield questionnaire (Oldfield,
1971) and showed no clinical signs of pareses or other movement
disorders. The study was approved by the ethics committee of the
University Clinics Freiburg. Platinum grid electrodes (4 mm
electrode diameter, 112 contacts, 7.1 mm inter-electrode distance)
weresubdurally implanted abovethefronto-parieto-temporal region
of the left hemisphere (Fig. 3a). The site of electrode implantation
The patients performed a self-paced center-out reaching task with
the right arm in four directions (right, left, forward, backward, target
distance 20 cm) identical to the task used in the EEG experiments
(for minor differences, see Supplementary Methods). Electrocorti-
cograms (ECoG) were recorded using a clinical AC EEG-System
(IT-Med, Erlangen, Germany) at 256 Hz (P1) and 512 Hz (P2)
sampling rate and 5 s time constant (corresponding to a high-pass
filter with 0.032 Hz cutoff frequency) using an intracranial channel
as the reference electrode. Onset and end of arm movements were
determined based on digital video (25 Hz sampling rate)
synchronized to the ECoG. Details on electrical cortical stimulation
and on the anatomical assignment of electrode positions are given in
the Supplementary Methods.
2Several different definitions of frequency bands above the classical
gamma range have recently been proposed: “high-frequency oscillations”
from 80 to 500 Hz (Staba et al., 2002), “high gamma frequency” from 60-
200 Hz (Brovelli et al., 2005), “very high frequency oscillations” from 80 to
200 HZ (Gonzalez et al., 2006). In the present study we refer to the range
from 50 to 150 Hz as ‘‘high gamma band’’.
303T. Ball et al. / NeuroImage 41 (2008) 302–310
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EEG and ECoG analysis
Both EEG and ECoG data were first re-referenced to a common
average reference. EEG trials contaminated with artifacts (excessive
EMG, blinks, eye movements, head movements) were discarded
from further analysis. From a total number of 820 trials (median
per subject (range: 337–591 trials) thus entered the analysis of onset
aligned data after artifact rejection. For the analysis of movement
313–618 trials) was used. Thus, on average, 36% of the recorded
trials were discarded from further analysis.
For the EEG analysis, two sets of data were generated, one using
movement onset as triggers and one using movement end, i.e. the
time point when the hand reached the target, as triggers. Analysis
time windows were from 680 ms before to 560 ms after movement
onset and from 560 ms before to 680 ms after movement end. For
analysis of the ECoG data, ‘center-out’ and ‘center-in’ trials were
also pooled (164 in total in P1, 204 in P2). Analysis time windows
were from 1 s before to 3 s after movement onset. The time window
for the ECoG analysis was chosen longer than for the EEG, because
longer EEG time windows would have resulted in a higher number
of EEG trials to be rejected because of contamination by artifacts. In
case of the ECoG, there was no significant artifact problem and
without substantially losing data. Time-resolved amplitude spectra
of all EEG, ECoG, and EMG channels were calculated for each trial
individually by a multi-taper spectral analysis method (Percival and
Walden, 2002) using 2 slepian tapers and a time window of 320 ms
duration, time step was 40 ms. We computed relative power spectra
for each trial in the following way: for each frequency bin, we
dividedthetime-resolvedamplitudeby themean baseline amplitude
for this frequency. Both for EEG and ECoG, the baseline amplitude
wasdeterminedas thepowerinthefirst analyzedtime binbeforethe
arm movement onset. I.e. also the time window around movement
end was analyzed relative to the baseline defined before movement
onset. Then, the mean across all trials was computed and the Z-
scores of the spectral power changes relative to baseline activity
were determined for each subject. Spectral changes were considered
as significant if the median Z-score across subjects was greater than
3.76, corresponding to a p-value of pb0.01 that was Bonferroni
corrected for the multiple comparisons made for the 59 electrode
test for significant effects for each single bin of the time-frequency
hypotheses derived from the intracranial recordings. In particular,
around movement onset and movement end. On the other hand,
evaluation of the whole scalp topography of the EEG was an
individual resolution elements of the whole time-frequency plane.
All group averages were based on the median across subjects to
prevent group results that could be due to outliers in the individual
As an additional analysis, ECoG data was split according to the
movement duration (the time from movement onset to movement
end) into the half of trials with below median movement duration
(“fast” movements) and the half of trials with above median
movement duration (“slow” movements). Average spectral power
changes were then separately determined for both data sets.
Reaching movement durations showed considerable variability
between subjects (median movement duration ranging from 0.43 s
to 1.12 s, Table 1). Consequently, transient effects time-locked to
movement end might not have been (optimally) detected, if only
data aligned to movement onset had been analyzed. Therefore, as
described in the Methods section, EEG analyses were carried out
both on data aligned to movement onset and to movement end
(defined as the point of time when the target button was pressed).
for the frequency range from 0.05 to 150 Hz (EEG) and 0.032 to
128 Hz (ECoG) in Fig. 1. Both in the EEG and ECoG, HGB activity
increased around movement onset in the 60–90 Hz range and
became most pronounced at reaching movement end, extending to
frequencies of up to 130 Hz. Furthermore, similar power decreases
in the lower frequencies (alpha and beta bands) were evident in both
EEG and ECoG results. In both of the patients, we found
significantly increased HGB power in motor cortical channels in
the 60 to 90 Hz range, both around movement onset and around
movement end (pb0.01). Of the individual EEG subjects, 4 of the 8
subjects showed a significant peak of HGB activity at electrode
position C3 at movement onset (i.e. in the time window −100 ms to
100 ms around movement onset, pb0.01, corrected) and 6 of the 8
subjects showed a significant peak at C3 at movement end.
Time-resolved topographies of spectral EEG power changes
across the whole gamma band up to 130 Hz are shown in Fig. 2 both
for the time window around movement onset (Fig. 2a) and for the
time window around movement end (Fig. 2b, median across
subjects). In both cases, the most pronounced power increases were
within the 60–85 Hz range. Across subjects, significant power in-
creases (pb0.01, corrected for multiple comparisons, see Methods)
first occurred around 240 ms after movement onset. Around
movement end, a significant spectral power increase at electrode
position C3 (i.e. above the region of left sensorimotor cortex, see
Discussion) was present in the frequency range from approx. 30 to
130 Hz. Furthermore, significant spectral power increases both at
movement onset and end were also observed above the frontal
midline (electrode position FZ).
Results of the time-frequency analysis of the ECoG data of P1
together with the functional mapping obtained by direct cortical
electrical stimulation are given in Fig. 3. The most pronounced
HGB activity was localized at electrode positions recording from
the precentral gyrus and showing hand and arm motor responses
upon electrical stimulation. Clear modulation of gamma activity
reached from approx. 50 Hz up to the highest frequency that could
be studied (128 Hz). In several channels, HGB activity showed a
Timing information of arm movement. Median, lower (IQR1), and upper
(IQR2) limits of the interquartile range of movement durations for the
subjects of the EEG study (S1–S8, sorted according to median movement
S1 S2S3S4 S5 S6 S7 S8
Movement duration was defined as the time interval from releasing the start
point until reaching the target.
304T. Ball et al. / NeuroImage 41 (2008) 302–310
Author's personal copy
biphasic time course such as observed at electrode position E4 in
P1 (c.f. Fig. 3a for orientation), located on the precentral gyrus and
showing finger movement responses upon electrical stimulation.
For this representative ECoG channel, further time-frequency plots
are shown in Fig. 4. Data was split according to movement
duration in ‘fast movements’ and ‘slow movements’. In both cases,
Fig. 1. Similarity of movement related EEG and ECoG high gamma band spectral power changes. (a) EEG movement related spectral power changes (median
across all subjects). Results are shown for electrode C3 approx. above the primary sensorimotor hand area contralateral to the side of the reaching movements,
both for movement onset and end. Overall, HGB increases were most pronounced in the approx. 60 to 85 Hz band as indicated by the black dashed lines.
Additionally, power decreases in the lower frequencies (alpha and beta bands) can be seen. A relative power of 1 is identical to no change relative to baseline;
values between 0 and 1 correspond to a power decrease and values greater than 1 to a power increase. Scalp positions of the channels C3, C4, CZ, and FZ are
marked in the upper inset showing the EEG electrode layout. (b) ECoG movement related spectral power changes in motor cortex channels (mean across 20
channels from 2 patients) with hand or arm motor responses upon electrical cortical stimulation. Positions of motor cortex channels of P1 are marked red in lower
inset showing the ECoG electrode layout of patient 1. The same frequency range as in (a) from approx. 60 to 85 Hz is again marked by the black dashed lines. As
for the EEG, at movement onset, high gamma band (HGB) power increases predominated in this frequency range. Also as in the EEG, the HGB response at
movement end was stronger as compared to movement onset and involved a broader frequency band up to approx. 130 Hz. Additionally, again as in the EEG,
powerdecreasesin the lowerfrequencies(alpha andbeta bands) canbe seen. Additionally, in the last time bins of the movementend related time window, there is
a power rebound in the upper beta range extending to the lower gamma band. Both in the EEG and ECoG plots, the frequency range around 50 Hz is marked by
white lines. Little relative power changes were detectable in this range, probably due to line hum. The highest frequency that could be investigated in the ECoG
was 128 Hz (due to the sampling frequency of 256 Hz) while EEG results are shown up to 150 Hz.
305T. Ball et al. / NeuroImage 41 (2008) 302–310
Author's personal copy
broad-band HGB activity occurred first around movement onset
and a second time around movement end, indicating that this
biphasic time course was indeed related to movement duration.
of oscillatory brain activity and voluntary movement for the lower
frequency components of human electro-encephalographic (EEG)
recordings up to the low gamma range around 40 Hz (Omlor et al.,
2007; Pfurtscheller et al., 1993; Pfurtscheller and Neuper, 1992).
High gamma band (HGB) power increases related to self-paced
movements inexperimentalparadigmswithout anyvisualcues have
been previously demonstrated in invasive recordings in epilepsy
patients with subdural recordings (Ball et al., 2004; Miller et al.,
2007; Ohara et al., 2000; Pfurtscheller et al., 2003), intracortical
Fig. 2. Topography of movement related EEG high gamma band activity. (a) Results for the time window around movement onset. Each of the topographic plots
(anterior to the top) shows the median scalp distribution of Z-scores of relative power changes, representing a time and frequency bin as indicated on the X- and
Y-axes. Electrode positions where the median Z-score corresponded to a p-valueb0.01 (Bonferroni corrected for multiple comparisons for the 59 electrode
positions) are marked by white disks. Significant power changes were observed 240 ms after movement onset at electrode position C1, approx. located above the
primary sensorimotor arm area, in the frequency bin centered approx. at 72 Hz (59 to 84 Hz) and at electrode position FZ above the frontal midline, presumably
recording from the prefrontal cortex or the anterior part of the supplementary motor area (SMA). (b) Results for the time window around movement end. As in
Fig. 2a, each of the topographic plots shows the median scalp distribution of Z-scores of relative powerchanges. Electrodepositions with significantmodulations
(pb0.01)aremarkedby whitedisks.Asfor movementonset,themost pronounced effectswere seeninthe 59 to84 Hzband.In this band,powerincreasesstarted
well before movement end (=the time when the hand reached the target). More broad-band power increases from approx. 30 to 130 Hz were found at electrode
position C3 shortly after movement end. Significant effects, particularly in the 59 to 84 Hz band, also extended to regions including the frontal midline.
306T. Ball et al. / NeuroImage 41 (2008) 302–310
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Fig.3. Topographyof movement related ECoG highgamma bandactivity. (a)Positionof subdurallyimplantedelectrodes (P1).A squareelectrode array with118
contacts (marked blue) was implanted above the left fontal cortex, also covering parts of the anterior parietal and superior temporal lobe. Two additional
electrodes stripes were implanted above the anterior temporal lobe and in the fronto-polar region. The electrode positions showing seizure onset, frequent
interictal spikes, and rare interictal spikes are color coded. The resection performed subsequently to the invasive diagnostics for treatment of the subject's
epilepsy is marked in grey. (b) Time-frequency plots of relative spectral power changes for the part of the ECoG grid covering motor cortex as marked by a red
box in (a). Time (x) axis runs from 1 s before to 3 s after movement onset, the frequency (y) axis ranges from 0.032 to 128 Hz; the depicted range of relative
power changes is from 0.4 to 3. Plots from primary motor cortex (M1) channels (see Supplementary Methods for the definition of M1 channels used) are marked
with black boxes. Letters within the subplots indicate electrical stimulation responses (L — Leg, A — Arm, H — Hand, E — Eye, F — Face; capital letters:
motor responses, minor letters: somatosenory responses). Further results from data from the channel marked by an asterix (channel E4) are shown in Fig. 4.
307 T. Ball et al. / NeuroImage 41 (2008) 302–310
Author's personal copy
using scalp recorded EEG in healthy human subjects and that the
intracranial data from epilepsy patients obtained during the same
motor task. Furthermore, our results suggest that a major site of
the precentral motor cortex contralateral to the side of movement.
The power density of EEG and local field potential (LFPs) in the
mammalian cortex are, overall, inversely proportional to frequency
(Freeman et al., 2000). Therefore, gamma band oscillations in
general, and the HGB in particular, are of low amplitude compared
to alpha and beta band oscillations. Furthermore, the HGB in EEG
recordings are prone to artifact contamination, especially caused by
et al., 2003; Whitham et al., 2007). Subtle gamma modulations may,
therefore, be missed due to their typically low signal-to-noise ratio.
In the present study, we optimized our approach for detecting low
amplitude HGB activity in several respects. First, we chose a motor
low amplitude of HGB activity, we recorded EEG at high am-
plification (×12,500), similar to a study that was previously
successful in detecting gamma bursts up to 150 Hz in a visuo-motor
task (Krieger and Dillbeck, 1987). Third, for spectral analysis, we
investigated the entire gamma range up to 128 Hz using a multi-
tapering method suitable for signal-to-noise improvement (Percival
movement related EEG activity both in the group results (median
across subjects, Fig. 1,2) and also in the majority of the individual
subjects (see also Supplementary Fig. 1).
The 60–85 Hz range where we found movement related HGB
EEG modulation lies above the range investigated in previous EEG
studies using ‘pure’ motor tasks, but well within the range where
invasive studies previously found movement related activity, both in
human and monkey motor cortex. However, although investigated,
no power changes in the 70–90 Hz range were found in several
studies using visuo-motor tasks (Conway et al., 1995; Schoffelen
et al., 2005), presumably reflecting task differences and/or
differences in the recording/analysis methods used. On the other
side, also using visuo-motor and auditory reaction time tasks, EEG
described up to 150 Hz (e.g. Gonzalez et al., 2006; Krieger and
Dillbeck, 1987; Shibata et al., 1999). To what extent these results
were caused by the visual cues used in these studies, however, is
difficult to judge, particularly because HGB activity in the EEG can
also be evoked by passive visual stimulation (Cobb and Dawson,
responses during visuo-motor tasks unlikely to result from the visual
by Leuthardt et al. (2007).
Based on the results of our intracranial data obtained using the
same movement task as used in the EEG experiment we expected
increased HGB power in EEG electrodes recording from the hand
and arm representations of the left pre- and postcentral gyrus. In the
international 10–10 electrode system, the two electrode positions
that are most likely positioned above this area are electrodes C1 and
C3 (Okamoto et al., 2004). Indeed, we found a transient, focal
enhancement of HGB activity in electrodes C1 and C3 around
movement onset and at movement end (Figs. 1, 2). An important
question is whether these results could possibly be due to EMG
contamination. Across subjects, EMG artifacts typically are
strongest at the outer electrode positions near face, temporalis, and
neck muscles (Goncharova et al., 2003). In contrast, the HGB
activity that we have observed was maximal at electrode positions
EMG origin. Only around movement end, gamma band increases
were also observed at peripheral (occipital) electrodes (Fig. 2)
indicating a transient EMG contribution in this region, most likely
from contraction of neck muscles. Channels with clear HGB
a clear decrease in the lower frequencies, i.e. the well known so-
called movement related to ‘desynchronization’ (Pfurtscheller et al.,
1992; Pfurtscheller et al., 2003). In contrast, EMG is characterized
by increased spectral power also in these lower frequencies
(Goncharova et al., 2003) and also extends well above 130 Hz,
again supporting a neural rather than an EMG origin of the gamma
band EEG modulations above sensorimotor cortex described in the
present study. Furthermore, a neuronal origin is also strongly
indicated by the high similarity of the EEG spectra to those of
intracranial data recorded from the same brain region (Fig. 1).
Besides the left central region, HGB enhancement was also
observed above the frontal midline including electrode position FZ
position FZ (Okamoto et al., 2004) is above the medial prefrontal
anterior border of area 6 (Eickhoff et al., 2005), indicating that the
signals at FZ originated most probably in medial prefrontal cortex or
maybe in the anterior part of the supplementary motor area (SMA).
Fig. 4. High gamma band ECoG responses and movement duration. Data
recorded fromchannelE4 of P1 located on the precentral gyrus (cf. Fig. 3 for
anatomical orientation) were split according to movement duration into fast
and slow movements (cf. Methods). In both plots, the first solid black line
indicates movement onset and the second solid black line movement end
(median across all fast and slow trials, respectively). 25th and 75th
percentiles of movement duration are indicated by the dashed lines. Median
movement times for the fast and slow trials were approximately 0.8 s and
1.2 s, respectively. The mean time course of HGB spectral power changes
(averaged from 50 to 128 Hz) and its standard deviation is shown in red.
Additionally, average relative spectral EMG activity for fast and slow
movements is shown (white-to-blue colorscale). For both fast and slow
movements, a first HGB peak can be seen at movement onset and a distinct,
second peak at movement end.
308T. Ball et al. / NeuroImage 41 (2008) 302–310
Author's personal copy
Intracranial recordings from these areas during the same movement
task would be an important step to further establish and delineate the
cortical origin of the HGB effects that we found above the frontal
with subdurally implanted electrodes performing the same motor
task as used in the EEG experiments. An advantage of ECoG as
compared to scalp EEG is that through the close contact to the
grid, activity can be mapped with higher reliability and spatial
accuracy (Niedermeyer and Lopes da Silva, 2004). Time-frequency
analysis of the ECoG data revealed broad-band gamma responses
bothatmovementonsetand end(Figs.1,3,4)thatincluded boththe
most pronounced at electrode locations on the precentral gyrus that
showed hand and arm motor responses upon electrical stimulation.
These results suggest that the precentral motor cortex and not the
postcentral somatosensory cortex may be the predominant source of
the focal gamma band activity observed in the EEG at C3 and C1
electrode positions. To further delineate the location of the EEG
sources of movement related HGB activity, however, electrical
source analysis techniques (Ball et al., 1999; Fuchs et al., 1999;
Ilmoniemi, 1991) might be useful.
EEG and ECoG fit well to the previous ECoG observations during
visually cued hand movements (Zygierewicz et al., 2005) and
intracortical recordings during self-paced finger movements (Szur-
haj et al., 2005). In the latter study, increased HGB activity of a
single recording channel was either found in the time window
around EMG onset or EMG end, but never in both (Szurhaj et al.,
2005). This difference to our data and also to the results of
Zygierewicz et al. (2005) may be related to the recording techniques
(intracortical vs. epicortical), to the movement paradigm (simple
finger extension vs. natural, goal-directed arm movement), or to the
exact frequency band investigated (40–60 Hz in the work of Szurhaj
et al. 2005). More research will be necessary to clarify this issue and
to establish, more generally, the relation between motor cortical
HGB, muscle activity, and different movement and task parameters.
In respect to their frequency range, the HGB activity we found
in the ECoG recordings extended up to the highest frequency that
we have investigated, i.e. up to 128 Hz. Pervious intracranial data
indicates that movement related HGB modulation in humans
extends even further, up to frequencies of 150 to 200 Hz (Brovelli
et al., 2005; Crone et al., 2006; Leuthardt et al., 2004; Miller et al.,
2007). Movement related HGB responses in the EEG were most
pronounced in the range of 60–85 Hz, but our results suggest that
they might also extend to even higher frequencies (Fig. 1, Suppl.
Fig. 1). Our study was however not designed to statistically
evaluate the whole time-frequency plane in detail, but instead we
concentrated on an a priori frequency band of interest (60–85 Hz)
based on the responses found in the ECoG. Otherwise, the multi
comparison problem due to the large number of independent time-
frequency resolution elements might have reduced the chance for
detecting HGB responses at all. Our results indicate that future
studies of movement related EEG changes should not restrict
themselves to the range of 60–85 Hz, but might also consider even
higher frequencies using statistical methods for assessing changes
in the whole time-frequency plane (Zygierewicz et al., 2005).
Further investigations using similar methods as in our study
may also probe the role of HGB EEG activity in different sensory
systems and in respect to cognitive function (Crone et al., 2001;
Kaiser et al., 2008; Lutzenberger et al., 2002; Ray et al., 2008). A
further future issue for the exploration of the HGB in EEG arises
due to the fact that for a large part we relied on group average
results. Some of the individual subjects of the present study did not
show significant gamma effects. Either these subjects were really
lacking a motor cortical HGB increase or it just remained
undetected by the present EEG methods. An argument for the
later explanation is the fact that the available intracranial data
shows movement related high gamma increases with high inter-
individual reproducibility, such as that in all 22 subjects of a recent
study (Miller et al., 2007). Further increasing the sensitivity of
EEG to gamma band modulation would therefore be a desirable
goal. It is currently not clear, to what extent the choice of
experimental paradigm, of recoding parameters such as amplifica-
tion and sampling rate, and of the analysis techniques each
contribute to successful detection of HGB activity in the EEG. The
importance of each of these factors should be further investigated
and might also be further optimized. More reliable within-subject
detection of HGB changes would also be necessary for potential
diagnostic application of HGB measurement using scalp EEG.
The research was supported by the WIN-Kolleg of the
Heidelberg Academy of Sciences and Humanities and the German
Federal Ministry of Education and Research (BMBF-DIP META-
COMP and BMBF grant 01GQ0420 to the BCCN Freiburg).
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
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