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Time-Frequency Analysis of Chemosensory Event-
Related Potentials to Characterize the Cortical
Representation of Odors in Humans
Caroline Huart
1,2
*, Vale
´ry Legrain
1,3
, Thomas Hummel
4
, Philippe Rombaux
1,2
, Andre
´Mouraux
1
1Institute of Neuroscience (IONS), Universite
´Catholique de Louvain, Brussels, Belgium, 2Department of Otorhinolaryngology, Cliniques Universitaires Saint-Luc, Brussels,
Belgium, 3Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium, 4Department of Otorhinolaryngology, Dresden Medical School,
Technical University, Dresden, Germany
Abstract
Background:
The recording of olfactory and trigeminal chemosensory event-related potentials (ERPs) has been proposed as
an objective and non-invasive technique to study the cortical processing of odors in humans. Until now, the responses have
been characterized mainly using across-trial averaging in the time domain. Unfortunately, chemosensory ERPs, in particular,
olfactory ERPs, exhibit a relatively low signal-to-noise ratio. Hence, although the technique is increasingly used in basic
research as well as in clinical practice to evaluate people suffering from olfactory disorders, its current clinical relevance
remains very limited. Here, we used a time-frequency analysis based on the wavelet transform to reveal EEG responses that
are not strictly phase-locked to onset of the chemosensory stimulus. We hypothesized that this approach would
significantly enhance the signal-to-noise ratio of the EEG responses to chemosensory stimulation because, as compared to
conventional time-domain averaging, (1) it is less sensitive to temporal jitter and (2) it can reveal non phase-locked EEG
responses such as event-related synchronization and desynchronization.
Methodology/Principal Findings:
EEG responses to selective trigeminal and olfactory stimulation were recorded in 11
normosmic subjects. A Morlet wavelet was used to characterize the elicited responses in the time-frequency domain. We
found that this approach markedly improved the signal-to-noise ratio of the obtained EEG responses, in particular, following
olfactory stimulation. Furthermore, the approach allowed characterizing non phase-locked components that could not be
identified using conventional time-domain averaging.
Conclusion/Significance:
By providing a more robust and complete view of how odors are represented in the human brain,
our approach could constitute the basis for a robust tool to study olfaction, both for basic research and clinicians.
Citation: Huart C, Legrain V, Hummel T, Rombaux P, Mouraux A (2012) Time-Frequency Analysis of Chemosensory Event-Related Potentials to Characterize the
Cortical Representation of Odors in Humans. PLoS ONE 7(3): e33221. doi:10.1371/journal.pone.0033221
Editor: Efthimios M. C. Skoulakis, Alexander Flemming Biomedical Sciences Research Center, Greece
Received October 11, 2011; Accepted February 9, 2012; Published March 9, 2012
Copyright: ß2012 Huart et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Dr. Huart is MD PhD student supported by the Fund for Scientific Research (F.R.S.-FNRS) of the French-speaking Community of Belgium. Dr. Legrain is a
postdoctoral researcher supported by the Research Foundation Flanders (FWO), Belgium. Dr. Mouraux has received support from the Pain Research EFIC-
Gru
¨nenthal Grant 2008 (EGG), the IASP Early Career Research Grant, and a from a Marie Curie European Reintegration Grant (ERG). The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: caroline.huart@uclouvain.be
Introduction
In 1978, Kobal and Plattig [1] introduced a device capable of
delivering transient chemosensory stimuli over the olfactory
neuroepithelium of the nasal mucosa, without concurrent
mechanical and/or thermal stimulation. The stimulator opened
new perspectives for exploring how the human brain processes
odors, through the non-invasive recording of time-locked chemo-
sensory event-related brain potentials (ERPs) [2,3,4,5]. Until then,
the use of electrophysiological techniques for the functional
exploration of olfaction in humans had remained limited, mainly
because of the lack of adequate methods to produce a selective,
controlled and transient chemosensory stimulus [6], and the fact
that pioneering studies had yielded conflicting results [7,8,9]. Early
stimulation methods relied on the delivery of brief air puffs
containing a given odorant. Inevitably, the sudden increase in
airflow associated with the presentation of the air puff activated
mechanically-sensitive trigeminal afferents, and determining
whether the elicited brain responses were triggered by the odorant
or by concurrent mechanical stimulation of the nasal mucosa was
difficult. In contrast, the device reported by Kobal and Plattig [1]
delivers pulses of odorant embedded within a constant airflow,
thus avoiding any concomitant mechanical stimulation of the nasal
mucosa, making it possible to study the brain responses related
specifically to the activation of chemosensitive afferents. Further-
more, using specific odorants, the device can be used to activate
olfactory and trigeminal chemosensory receptors relatively selec-
tively. For example, 2-phenylethanol can be used to elicit
chemosensory ERPs related to activation of olfactory afferents,
while gaseous CO
2
can be used to elicit chemosensory ERPs
related to the activation of trigeminal afferents [10,11,12].
The ability to characterize, non-invasively, the cortical processing
of chemosensory stimuli in humans is not only of interest for
research in the neuroscience of odor perception. Indeed, the
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technique is used as a clinical diagnostic tool [13,14]. Recent studies
have shown that disorders of olfaction are extremely common
(affecting up to 20% of the general population; [15]) and, although
olfactory dysfunction is often unnoticed, it has been shown to impact
significantly on the quality of life [16]. Finally, several studies have
underlined the potential diagnostic value of olfactory dysfunction,
which could constitute a premonitory sign of several neurodegen-
erative diseases, in particular, idiopathic Parkinson’s disease and
Alzheimer-type dementia [17,18,19,20,21,22].
Until now, the electroencephalographic (EEG) responses to
chemosensory stimulation have been identified mainly using
across-trial averaging in the time domain, a procedure which
cancels out changes in the EEG signal that are not time-locked to
the stimulus onset and that are not strictly stationary across trials
and, thereby, enhances the signal-to-noise ratio of time-locked
ERPs [11,23,24,25,26]. Using such an approach, the EEG
responses to chemosensory stimulation have been characterized
as a negative wave peaking approximately 320–500 ms after
stimulus onset (N1), followed by a positive wave peaking
approximately 450–800 ms after stimulus onset (P2 and/or P3)
[2,11,27,28,29]. Regardless of the type of chemosensory stimulus,
all of these responses are maximally recorded over the scalp
midline, whereby the N1-P2 complex elicited by olfactory
stimulation is sometimes described as displaying a slightly more
parietal scalp distribution than the N1-P2 complex elicited by
trigeminal stimulation [2,30,31].
Although it has been demonstrated that the latency and, to a
lesser extent, the amplitude of chemosensory ERPs are relatively
reproducible within subjects [32]; chemosensory ERPs - in
particular, olfactory ERPs – usually exhibit a very low signal-to-
noise ratio [10,26,33]. For example, Lo¨tsch and Hummel [10]
could not identify any reproducible olfactory ERP in approxi-
mately 30% of normosmic subjects. Hence, although the recording
of chemosensory ERPs is considered as a technique having great
potential, its current usefulness remains very limited, particularly
in the context of clinical diagnosis.
Here, we hypothesized that the low signal-to-noise ratio of
chemosensory ERPs could at least in part be due to an important
amount of temporal jitter affecting the brain responses to
chemosensory stimulation. Indeed, the existence of a significant
amount of temporal jitter would imply that the elicited EEG
responses are no longer strictly stationary across trials and,
hence, that these EEG responses will be distorted or even
cancelled-out using conventional across-trial averaging proce-
dures performed in the time domain. The hypothesized jitter
would result from the different steps separating the onset of the
chemosensory stimulus and the generation of cortical responses.
Using patch clamp recordings, the currents elicited by brief
pulses of odors have been characterized by latencies ranging
from 175 to 600 ms [34], thus indicating that the intracellular
chemosensory transduction steps can constitute a significant
source of latency jitter. Furthemore, Guetchell et al. [35] showed
a relationship between odor concentration and response latency
in preparations including a mucus layer, most probably because
diffusion speed across the mucus layer is dependent on odor
concentration. Hence, inevitable trial-by-trial variations in the
physical properties of the olfactory stimulus – in particular, the
relative position of the tube ending used to deliver the stimulus
relative to the olfactory epithelium – may be expected to induce
significant trial-by-trial latency jitter in the neural activity
triggered within the olfactory receptor neurons. An additional
significant source of variability may be the influence of the
respiratory dynamics on bulbar and cortical activity (reviewed in
[36]).
For this reason, in order to increase the signal-to-noise ratio of
chemosensory EEG responses, we used an alternative approach to
reveal EEG activity that is induced by the stimulus (and, thereby,
related to its cortical processing), but not sufficiently stationary
across trials to be revealed by classic averaging in the time domain.
The approach relied on a time-frequency decomposition of single-
trial EEG epochs performed using the continuous wavelet
transform [25,37], such as to express signal amplitude as a
function of time and frequency, regardless of phase [25,37].
Across-trial averaging of the obtained time-frequency maps of
EEG amplitude yielded the average signal amplitude as a function
of latency and frequency, within which transient increases or
decreases of EEG amplitude could be identified. As suggested by
the results of previous studies [37,38], the approach may be
expected to reveal ERPs even if these are not strictly stationary
across trials because of a significant amount of latency jitter.
Hence, the approach could be more effective at revealing
chemosensory ERPs. Furthermore, because across-trial averaging
in the time-frequency domain enhances time-locked EEG changes
regardless of whether these changes are phase-locked across trials,
the procedure can effectively enhance stimulus-induced modula-
tions of the amplitude of ongoing EEG oscillations (event-related
synchronization and desynchronization, ERS and ERD respec-
tively), hypothesized to reflect mechanisms involved in cortical
activation and deactivation underlying sensory-motor and cogni-
tive functions [39,40,41].
Using this approach, the aims of the present study were thus (1)
to characterize for the first time the phase-locked and non phase-
locked EEG responses to trigeminal and olfactory stimulation and
(2) to examine whether this approach may be used to enhance the
signal-to-noise ratio of the obtained EEG responses and, thereby,
increase their potential usefulness as a research and clinical
diagnostic tool to study olfaction in humans.
Results
Across-trial averaging in the time domain
Trigeminal stimulation. As shown in the group-level average
waveforms displayed in Figure 1, trigeminal stimulation elicited a
negative deflection (TRI-N1: 391655 ms) followed by a positive
deflection (TRI-P2: 554657 ms). The scalp distribution of both
peaks was widely distributed over the two hemispheres, and
maximal at the scalp vertex (Figure 2). At electrode Cz, TRI-N1
amplitude was 3.562.0 mV and TRI-P2 amplitude was
7.064.5 mV.
The measure of TRI-N1 peak amplitude had a marginal
discrimination performance, with an AUC of 0.7360.11
(p = 0.038) (Table 1, Figure 3). This measure was thus only
marginally efficient for discriminating between presence vs.
absence of a trigeminal ERP and, hence, its relevance can be
questioned.
In contrast, the measure of TRI-P2 peak amplitude had a high
discrimination performance, with an AUC of 0.9160.06
(p,0.0001). Using the cutoff value associated with the greatest
Youden index, the measure of TRI-P2 peak amplitude had a
sensitivity of 81.2% and a specificity of 81.2% for discriminating
between presence vs. absence of a trigeminal ERP (Table 1,
Figure 3).
Olfactory stimulation. As shown in the group-level average
waveforms displayed in Figure 1, olfactory stimulation elicited
clearly identifiable negative and positive peaks in only a few
subjects. In these subjects, the olfactory ERP appeared maximal at
the vertex with a slightly more posterior distribution for OLF-N1
as compared to OLF-P2 (Figure 2).
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At electrode Cz, the estimated OLF-N1 latency and amplitude was
397627 ms and 20.961.3 mV, whereas the estimated OLF-P2
latency and amplitude was 6166109 ms and 4.262.4 mV. Neither
the measure of OLF-N1 amplitude nor the measure of OLF-P2
amplitude were able to discriminate significantly between presence
vs. absence of an olfactory response (OLF-N1 amplitude: AUC=
0.5060.13,p=0.975;OLF-P2amplitude:AUC=0.6060.13,
p = 0.429) (Table 1, Figure 3).
Across-trial averaging in the time-frequency domain
Trigeminal stimulation. As shown in the CWT-
AVERAGE transform, the time-frequency representation of the
phase-locked trigeminal chemosensory ERP consisted mainly in an
increase of signal amplitude peaking approximately 400 ms after
stimulus onset, and centered at relatively low frequencies
(approximately 1–5 Hz) (Figure 4). In addition to (1) this low-
frequency phase-locked EEG response, the CWT-SINGLE
transform revealed that trigeminal stimulation also elicited (2) a
significant long-lasting desynchronization of alpha-band (8–12 Hz)
oscillations starting approximately 600 ms after stimulus onset and
(3) a significant non phase-locked increase in EEG signal
amplitude peaking approximately 350 ms after stimulus onset
and centered around 10–15 Hz (Figure 5).
Based on these observations, three distinct time-frequency ROIs
were defined as follows. TRI-TF1: 200–600 ms and 2–7.5 Hz,
circumscribing the time-frequency representation of the phase-
locked trigeminal ERP. TRI-TF2: 900–1400 ms and 8–12 Hz,
circumscribing the stimulus-induced desynchronization of alpha-
band EEG oscillations. TRI-TF3: 300–450 ms and 10–17.5 Hz,
circumscribing the non phase-locked increase in EEG power
centered around 10–15 Hz (Figure 5).
The scalp topography of TRI-TF1 was maximal at the vertex
(Figure 2). At electrode Cz, the activity within TRI-TF1 peaked at
490674 ms and 3.560.9 Hz. The measurement of TRI-TF1
amplitude at electrode Cz and Pz had a perfect discrimination
performance (AUC = 1.0060.00, p,0.0001) (Table 1, Figure 3).
TRI-TF1 amplitude was thus able to discriminate between
presence vs. absence of a response with a sensitivity and specificity
of 100%. As compared to the measures of peak amplitude
performed in the time domain, the discrimination performance of
TRI-TF1 amplitude was significantly greater than the discrimi-
nation performance of TRI-N1 amplitude (difference in AUC:
0.2760.11, p = 0.016), but was not significantly different from the
discrimination performance of TRI-P2 amplitude (difference in
AUC: 0.0960.136, p = 0.133) (Table 1).
As compared to the scalp topographies of TRI-TF1, the scalp
topography of TRI-TF2 extended more towards posterior regions
(Figure 2). At electrode Pz, maximum desynchronization was
observed at 8116140 ms and 10.061.2 Hz. The discrimination
performance of TRI-TF2 was significant (AUC = 0.8960.08,
p,0.0001) (Table 1, Figure 3). Using the cutoff value associated
with the greatest Youden index, the measure of TRI-TF2 amplitude
had a sensitivity of 90.9% and a specificity of 90.9% for
discriminating between presence vs. absence of an EEG response
to trigeminal stimulation. This discrimination performance was not
significantly different from the discrimination performance of TRI-
N1 amplitude (difference in AUC = 0.1660.15, p = 0.277) and
TRI-P2 amplitude (difference in AUC = 0.0260.64, p = 0.746)
(Table 1).
Such as the scalp topography of TRI-TF1, the scalp topography
of TRI-TF3 was maximal over the vertex (Figure 2). At electrode
Cz, the response peaked at 383650 ms and 12.862.8 Hz. This
peak latency was significantly different from the TRI-TF1 peak
latency (t
10
= 5.04; p = 0.0005) and from TRI-P2 peak latency
(t
10
= 2.83; p = 0.018), but was not significantly different from
TRI-N1 peak latency (t
10
= 0.50; p = 0.629). The discrimination
performance of TRI-TF3 amplitude measured at Cz was
significant (AUC = 0.8660.09, p,0.0001) (Table 1, Figure 3).
Using the cutoff value associated with the greatest Youden index,
the measure of TRI-TF3 amplitude had a sensitivity of 81.8% and
a specificity of 90.9% for discriminating between presence vs.
absence of a trigeminal response. As compared to the measures of
peak amplitude performed in the time domain, the discrimination
performance of TRI-TF3 amplitude was not significantly different
from the discrimination performance of TRI-N1 amplitude
(difference in AUC = 0.1360.14, p = 0.343) and TRI-P2 ampli-
tude (difference in AUC = 0.0560.11, p = 0.633) (Table 1).
Olfactory stimulation. As shown in the CWT-AVERAGE
transform, olfactory chemosensory stimulation did not elicit any
clearly circumscribed phase-locked increase in EEG signal power
Figure 1. Trigeminal and olfactory chemosensory ERPs. Trigeminal and olfactory chemosensory ERPs recorded at the scalp vertex (Cz vs.
A1A2) in 11 healthy normosmic volunteers. Gaseous CO
2
(50% v/v) was used to selectively activate trigeminal afferents. 2-Phenylethanol (50%v/v)
was used to selectively activate olfactory afferents. 60 stimuli were presented, lasting 200 ms (20-ms rise-time), separated by a 30 s inter-stimulus
interval. Individual ERP waveforms are shown in light grey, while the group-level average waveform is shown in black. Note that trigeminal
chemosensory stimulation elicited a clear negative-positive complex (TRI-N1/TRI-P2), contrasting with the low signal-to-noise ratio of the response
elicited by olfactory chemosensory stimulation, which was clearly identifiable in only a few subjects.
doi:10.1371/journal.pone.0033221.g001
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(Figure 4). In contrast, the CWT-SINGLE transform revealed that
olfactory stimulation did elicit a number of significant non phase-
locked increases and decreases in EEG power, consisting mainly in
(1) a long-lasting increase in the amplitude of low frequencies
extending from approximately 300 ms to 1000 ms after stimulus
onset, centered around 5 Hz, followed by (2) a desynchronization
of alpha-band EEG oscillations, starting approximately 1000 ms
after stimulus onset (Figure 5).
Based on these observations, two distinct time-frequency ROIs
were defined as follows. OLF-TF1: 300–1000 ms and 3–7 Hz,
circumscribing the long-lasting increase in low frequency ampli-
tude extending centered around 5 Hz. OLF-TF2 1000–1300 ms
and 8–12 Hz, circumscribing the stimulus-induced desynchroni-
zation of alpha-band EEG oscillations (Figure 5).
The scalp topography of OLF-TF1 was maximal over fronto-
central regions (Figure 2). At electrode Fz, the response peaked at
7296242 ms and 5.161.4 Hz. At this electrode, the discrimina-
tion performance of OLF-TF1 amplitude was significant
(AUC = 0.8960.07, p,0.0001) (Table 1, Figure 3). Using the
cutoff value associated with the greatest Youden index, the
measure of OLF-TF1 amplitude had a sensitivity of 81.8% and a
specificity of 90.9% for discriminating between presence vs.
absence of an EEG response to olfactory stimulation. The
discrimination performance of OLF-TF1 amplitude was signifi-
cantly greater than the discrimination performance of the measure
of OLF-N1 (difference in AUC: 0.3960.148, p = 0.009) and OLF-
P2 (difference in AUC: 0.2960.14, p = 0.036) peak amplitudes
performed in the time domain (Table 1).
The scalp topography of OLF-TF2 was maximal at the vertex
(Figure 2). At electrode Cz, the desynchronization peaked at
1190698 ms and 9.661.7 Hz. The discrimination performance
of OLF-TF2 amplitude was not significant (AUC: 0.5760.13,
p = 0.589) (Table 1, Figure 3).
Here, the EEG was recorded using 64 scalp channels.
Considering that the EEG responses were consistently identified
at electrode Cz following trigeminal stimulation and electrode Fz
following olfactory stimulation, recording from these two locations
could be sufficient to characterize them, in particular, in a clinical
setting (to reduce the time required for setting up the recording, as
well as to reduce computation time).
Correlation between psychophysical olfactory
performance and EEG response magnitude
Previous studies have shown a relationship between the
psychophysical assessment of olfactory performance and the
magnitude of chemosensory ERPs [42]. For this reason, we
examined the correlation between the psychophysical TDI scores
and the magnitude of the significant EEG measures identified in
the time-frequency domain (TRI-TF1, TRI-TF2, TRI-TF3, OLF-
TF1) (Figure 6). We found a significant correlation between TDI
scores and the magnitude of the olfactory response OLF-TF1
(r = 0.70, p = 0.017). In contrast, there was no significant
correlation between TDI scores and the magnitude of the different
responses to trigeminal stimulation (TRI-TF1: r = 0.39, p = 0.233;
TRI-TF2: r = 20.58, p = 0.064; TRI-TF3: r = 0.02, p = 0.947).
Discussion
Using conventional across-trial averaging in the time domain,
trigeminal stimulation elicited consistent ERPs, consisting of a
negative wave (TRI-N1) followed by a positive wave (TRI-P2),
both maximal at the vertex. The magnitude of TRI-N1 was only
moderately effective at discriminating between the presence vs.
absence of a trigeminal response, but the magnitude of TRI-P2
had a high discrimination performance. Contrasting with the
relatively high signal-to-noise ratio of the ERPs elicited by
trigeminal stimulation, olfactory stimulation elicited a clearly
identifiable negative (OLF-N1) and positive (OLF-P2) peak in
only a few subjects, and neither the measure of OLF-N1
amplitude nor the measure of OLF-P2 amplitude were able to
discriminate between the presence vs. absence of an olfactory
response.
Figure 2. Scalp distribution of the EEG responses elicited by
trigeminal and olfactory chemosensory stimulation. The scalp
distribution of the EEG responses identified using across-trial averaging
in the time domain (TRI-N1, TRI-P2, OLF-N1, OLF-P2) are expressed in
microvolts, whereas the scalp distribution of the EEG responses
identified using across-trial averaging in the time-frequency domain
(TRI-TF1, TRI-TF2, TRI-TF3, OLF-TF1, OLF-TF2) are expressed as relative
percentage increase or decrease relative to baseline.
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As hypothesized, across-trial averaging in the time-frequency
domain markedly enhanced the signal-to-noise ratio of the elicited
responses, and revealed EEG activity that could not be identified
using conventional time-domain averaging. Following trigeminal
stimulation, three distinct responses were identified: TRI-TF1
which circumscribed the time-frequency representation of the
phase-locked trigeminal ERP, TRI-TF2 which circumscribed a
stimulus-induced desynchronization of alpha-band EEG oscilla-
tions and TRI-TF3 which circumscribed a transient non phase-
locked increase in EEG power centered around 10–15 Hz. The
discrimination performance of each of the three responses was
highly significant, with a perfect discriminatory performance for
TRI-TF1. Following olfactory stimulation, the enhancement of
signal-to-noise ratio was even more pronounced. Whereas no clear
olfactory ERP could be identified in the time domain, time-
frequency domain analysis revealed two significant non phase-
locked responses: a long-lasting increase in the amplitude of low
frequencies EEG oscillations (OLF-TF1), followed by a desyn-
chronization in the alpha-band (OLF-TF2). Most importantly, the
magnitude of OLF-TF1 effectively discriminated between the
presence vs. absence of an olfactory response.
EEG responses to trigeminal stimulation
In the present study, gaseous CO
2
was used to activate
trigeminal afferents selectively [43,44]. When applied to the nasal
mucosa in short pulses, gaseous CO
2
induces a short-lasting
painful stinging sensation [45], related to the activation of Ad- and
C-fibre nociceptive afferents of the ophthalmic and maxillary
branches of the trigeminal nerve, ending in the the nasal mucosa
[45].
Table 1. Discrimination performance of the phase-locked and non phase-locked EEG responses to trigeminal and olfactory
chemosensory stimulation.
ROI Latency (ms) Frequency (Hz) AUC p-value Sensitivity (%) Specificity (%)
TRI-N1 391655 0.7360.11 0.038 81.2 63.6
TRI-P2 554657 0.9160.06 ,0.0001 81.2 81.2
TRI-TF1 490674 3.560.9 1.0060.00 ,0.0001 100.0 100.0
TRI-TF2 8116140 10.061.2 0.8960.08 ,0.0001 90.9 90.9
TRI-TF3 383650 12.862.8 0.8660.09 ,0.0001 81.8 90.8
OLF-N1 397627 0.5060.13 0.975 36.4 31.8
OLF-P2 6166109 0.6060.13 0.429 45.4 90.9
OLF-TF1 7296241 5.161.4 0.8960.07 ,0.0001 81.8 90.9
OLF-TF2 1190698 9.661.7 0.5760.13 0.589 45.4 72.7
doi:10.1371/journal.pone.0033221.t001
Figure 3. Receiver Operating Characteristic (ROC) curves. ROC curves were computed to estimate the discrimination performance (ability to
discriminate between presence vs. absence of stimulation) of each of the different EEG responses identified using across-trial averaging in the time
domain (TRI-N1, TRI-P2, OLF-N1, OLF-P2) and across-trial averaging in the time-frequency domain (TRI-TF1, TRI-TF2, TRI-TF3, OLF-TF1, OLF-TF2). The
shaded areas represent the 95% confidence interval of the obtained curves.
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In the time domain, the obtained trigeminal ERPs (TRI-N1:
391655 ms; TRI-P2: 554657 ms) were similar to those reported
in the literature, consisting of a negative wave peaking 320–
450 ms after stimulus onset, followed by a positive wave peaking
450–800 ms after stimulus onset, both waves displaying a scalp
distribution maximal at the scalp vertex [2,46].
Time-frequency domain analysis of the obtained EEG responses
revealed that, in addition to the phase-locked chemosensory ERP
(TRI-TF1), trigeminal stimulation also elicited a long lasting
desynchronization of ongoing alpha-band EEG oscillations (TRI-
TF2) and an early, transient and non phase-locked increase in
EEG power peaking approximately 350 ms after stimulus onset,
Figure 4. Time-frequency representation of the phase-locked EEG responses to trigeminal and olfactory chemosensory stimulation
(CWT-AVERAGE). The time-frequency transform of the waveforms obtained by performing conventional across-trial averaging in the time domain
was used to identify EEG responses that were phase-locked across trials, as these are preserved by the averaging procedure. Signal amplitude (group-
level average, electrode Cz vs. A1A2) is expressed as percentage increase or decrease relative to baseline (20.4 to 20.1 s) (ER%). Note that the
trigeminal chemosensory ERP is mainly represented by an increase of low-frequency activities (1–5 Hz). Also note the lack of a clear EEG response
following olfactory stimulation.
doi:10.1371/journal.pone.0033221.g004
Figure 5. Time-frequency representation of the non-phase locked EEG responses to trigeminal and olfactory chemosensory
stimulation (CWT-SINGLE). Non-phase locked EEG responses were identified by performing across-trial averaging in the time-frequency domain, a
procedure which enhances time-locked EEG responses regardless of whether they are phase-locked to the onset of the stimulus. The upper panels
show the group-level average time-frequency maps of oscillation amplitude (group-level average; electrode Cz vs. A1A2), expressed as percentage
increase or decrease relative to baseline (20.4 to 20.1 s) (ER%). Note that, in addition to the phase-locked EEG response (TRI-TF1), trigeminal
stimulation also elicits event-related desynchronization in the alpha-band (TRI-TF2), as well as a transient and early increase of signal amplitude
centered around 10–15 Hz (TRI-TF3). Also note that olfactory stimulation elicits a long-lasting non phase-locked increase of signal amplitude centered
around 5 Hz (OLF-TF1), possibly followed by event-related desynchronization in the alpha-band (OLF-TF2). The lower panels highlight areas of the
time-frequency matrix where signal amplitude deviated significantly from baseline (one-sample t-test).
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centered around 10–15 Hz (TRI-TF3). The scalp topography of
TRI-TF 1 and TRI-TF3 were both maximal at the scalp vertex,
while the scalp topography of TRI-TF2 showed maximal
desynchronization over central-parietal regions.
These stimulus-induced modulations of the magnitude of
ongoing EEG rhythms are thought to result from a transient
decrease or increase in the synchrony of underlying neuronal
populations. Ongoing EEG oscillations in the alpha-band (8–
12 Hz) are usually considered to reflect ‘‘idle rhythms’’ or, even, to
reflect a mechanism of active cortical inhibition (for a review see
[40,47]). This view is supported by the observation that sensory
stimuli induce alpha-band desynchronization over the cortical
areas thought to be specifically involved in processing the stimulus
(for a review see [47]). Hence, the alpha-band ERD elicited by
trigeminal stimulation (TRI-TF2) may be hypothesized to reflect a
stimulus-induced transient activation of neuronal populations
involved in processing the trigeminal input. The functional
significance of the early and brief non phase-locked increase of
EEG power represented by TRI-TF3 is more speculative. It is
interesting to note that a similar response has also been reported
following the thermal activation of skin nociceptors [37], but also
following non-nociceptive tactile, auditory or visual stimulation
[48,49,50]. The response could reflect a stimulus-induced increase
of the magnitude of ongoing EEG oscillations (ERS), but could
also reflect a stimulus-evoked ERP affected by a significant
amount of latency jitter [25,37]. Indeed, ERP components subject
to a significant amount of temporal jitter will appear as non phase-
locked relative to stimulus onset, and this dephasing will affect
more greatly components of higher frequency. Importantly, the
latency of TRI-TF3 was slightly but significantly more precocious
than the latency of the phase-locked ERP response (TRI-TF1),
thus suggesting that TRI-TF3 reflected earlier and, hence,
potentially more specific aspects of trigeminal cortical processing.
EEG responses to olfactory stimulation
In the present study, activation of olfactory afferents was
achieved using 2-phenylethanol, which is usually described as a
pleasant floral odorant.
After time-domain averaging, olfactory ERPs were identifiable
in only a few subjects. When present, the response consisted of a
negative wave (OLF-N1: 397627.48 ms) followed by a positive
wave (OLF-P2: 6166109.28 ms), maximal at the vertex, compat-
ible with what has been described previously [2,30,31]. Crucially,
the fact that we could not identify olfactory ERPs in a significant
number of normosmic subjects was entirely expected, as previous
studies have also reported that the signal-to-noise ratio of olfactory
ERPs is relatively poor [10,26]. For example, Lo¨ tsch and Hummel
[10] stated that they were unable to identify any reproducible
olfactory ERP in approximately 30% of normosmic subjects. This
was confirmed by our finding that neither OLF-N1 amplitude nor
OLF-P2 amplitude discriminated significantly between presence
vs. absence of an olfactory response.
Compared to trigeminal ERPs, several factors could contribute
to the lower signal-to-noise ratio of olfactory ERPs. First, possibly
because of its nociceptive nature, trigeminal stimulation is usually
perceived as much more sharp and intrusive than olfactory
stimulation. Hence, because chemosensory ERPs are thought to at
least partly reflect cortical processes involved in arousal and/or
stimulus-triggered attentional reorientation [51], the relatively low
amplitude of olfactory ERPs could be due to their relatively low
salience. In addition, it should be noted that stimuli were delivered
using a constant 30-s inter-stimulus interval (ISI), such as in several
other studies [52,53,54,55]. The regular occurrence of the stimulus
could thus have induced some amount of response habituation
[26]. Future studies could examine whether the use of variable ISIs
could further increase the signal-to-noise ratio of the elicited
responses. Second, residual odorant molecules could contaminate
the experimental environment, leading to an important reduction
of the signal-to-noise ratio of the actual stimulus. Third, the
cellular mechanisms involved in olfactory signal transduction
could be less phasic and stationary than those underlying the
transduction of trigeminal input and, hence, may be less suited for
the recording of time-locked EEG responses.
Nevertheless, time-frequency analysis of the EEG signals
following olfactory stimulation did reveal a number of significant,
non phase-locked EEG responses, consisting in a long-lasting
increase in the amplitude of EEG frequencies centered around
5 Hz (OLF-TF1), followed by a desynchronization of alpha-band
oscillations (OLF-TF2). The scalp topography of OLF-TF1 was
maximal at the vertex, and similar to the scalp topography of the
olfactory ERP. Hence, both responses could reflect the same
stimulus-evoked cortical activity, which would be markedly
Figure 6. Correlation between psychophysical olfactory performance and magnitude of the significant EEG responses to
chemosensory stimulation. The left panel (A) shows the scatter diagram illustrating the correlation between TDI scores and the olfactory EEG
response OLF-TF1. Note the significant positive correlation between the TDI score and OLF-TF1 magnitude (r = 0.70, p = 0.017). The right panel (B)
illustrates the correlation between TDI scores and trigeminal EEG responses (TRI-TF1 (blue), TRI-TF2 (red) and TRI-TF3 (green). Note the absence of
significant correlation between the TDI score and these different responses to trigeminal stimulation.
doi:10.1371/journal.pone.0033221.g006
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PLoS ONE | www.plosone.org 7 March 2012 | Volume 7 | Issue 3 | e33221
dephased by a large amount of temporal jitter, thus explaining
why the response appears largely non phase-locked relative to
stimulus onset, and why its average duration appears long and ill-
defined.
Discrimination performance
As expected from their relatively high signal-to-noise ratio, the
TRI-N1 and TRI-P2 waves of trigeminal ERPs, characterized
using conventional time-domain averaging, were able to discrim-
inate between the presence vs. absence of a trigeminal response
with a high sensitivity and specificity (TRI-N1: p = 0.038,
Sensitivity: 81.2%, Specificity: 63.6%, TRI-P2: p,0.0001,
Sensitivity: 81.2%, Specificity: 81.2%). When characterizing these
responses in the time-frequency domain, discrimination perfor-
mance of these responses was even greater: in our sample of
healthy subjects, the magnitude of TRI-TF1 was able to
discriminate between presence vs. absence of a response with a
sensitivity and specificity of 100%. However, this improvement of
discrimination performance was not significant, mainly because
trigeminal ERPs can already be very effectively identified in the
time domain.
The discrimination performance of olfactory ERPs identified
using conventional time-domain averaging was very poor and,
within the present study conditions, close to chance level (OLF-
N1: p = 0.975, Sensitivity: 36.4%, Specificity: 31.8%, OLF-P2:
p =0.429, Sensitivity: 45.4%, Specificity: 90.9%). This observation
confirms the results of previous studies [10]. In contrast, the
discrimination performance of the EEG responses to olfactory
stimulation identified in the time-frequency domain was markedly
and significantly higher. Indeed, in our sample of normosmic
subjects, the low frequency non phase-locked EEG response
characterizing OLF-TF1 was able to discriminate between
presence vs. absence of a response with a sensitivity of 81.8%
and a specificity of 90.9% (p,0.0001).
Relationship with perception and clinical usefulness
The finding that the psychophysical assessment of olfactory
performance (TDI scores) correlated significantly with the
magnitude of the EEG response to olfactory stimulation (OLF-
TF1) but not with the different EEG responses to trigeminal
stimulation further supports the view that this response relates to
olfactory perception, and highlights its potential clinical usefulness
(see also Figure 7).
Future studies including larger cohorts of patients with olfactory
disorders and age-matched healthy controls are needed to confirm
the clinical usefulness of our approach. Of particular interest
would be to examine the relationship between functional measures
of olfaction (psychophysical and electrophysiological assessment of
olfaction) and structural measures of the olfactory system (e.g.
MRI-based evaluation of olfactory bulb volume and/or olfactory
sulcus depth) [56].
In summary, we show that, within the present study conditions,
time-frequency analysis of the EEG activity triggered by trigeminal
and olfactory stimulation can disclose responses that cannot be
identified using conventional time-domain averaging. Because the
non-phase locked responses identified using time-frequency
analysis are likely to reflect different aspects of the cortical
processing of chemosensory stimuli, the approach provides a more
complete view of how odors are represented in the human brain.
Furthermore, we show that our approach markedly enhances
the signal-to-noise ratio of the elicited EEG responses, in
particular, the responses elicited by olfactory stimulation. The
ability to detect, with a high sensitivity and specificity, the EEG
responses to olfactory stimulation is not only of interest for
neuroscientists aiming to understand the cortical processes
involved in the perception of odors in humans. Indeed, our
approach could also constitute the basis for a clinical diagnostic
tool for the clinical evaluation of patients complaining of olfactory
disorder, but also for the early differential diagnosis of certain
neurodegenerative diseases thought to be associated with olfactory
dysfunction. However, future studies will be needed to assess fully
the clinical usefulness of this approach.
Methods
Participants
After obtaining written informed consent, experiments were
performed in 11 healthy normosmic volunteers (3 females and 8
Figure 7. Clinical usefulness of time-frequency analysis of chemosensory ERPs. A. Time-frequency representation of the non-phase locked
EEG responses to trigeminal and olfactory chemosensory stimulation (CWT-SINGLE; see Methods) in one hyposmic patient (TDI = 23) and one anosmic
patient (TDI = 14). Signal amplitude is expressed as percentage increase or decrease relative to baseline (20.4 to 20.1 s) (trigeminal stimulation:
electrode Cz; olfactory stimulation: electrode Fz). B. Magnitude of TRI-TF1 and OLF-TF1 measured in the hyposmic patient (grey dot), the anosmic
patient (white dot) and the 11 healthy controls (black dot). The horizontal red line indicates the cutoff amplitude value associated with the greatest
Youden index. Note that the magnitude of OLF-TF1 measured in the hyposmic and anosmic patients are both below the cutoff value. In contrast,
note that the magnitude of TRI-TF1 measured in the two patients was similar to those measured in the healthy controls.
doi:10.1371/journal.pone.0033221.g007
Time-Frequency Analysis of Chemosensory ERPs
PLoS ONE | www.plosone.org 8 March 2012 | Volume 7 | Issue 3 | e33221
males), aged between 24 and 30 years. Investigations were
approved by the local Ethics Committee (Comite´ d’Ethique
Biome´dicale Hospitalo-Facultaire, Universite´ catholique de Lou-
vain, Faculty of Medecine, Nu: B40320108777). In order to ensure
that participants were normosmic, orthonasal olfactory function
was assessed using the validated Sniffin’ Sticks test [57,58]. In this
test, odors are presented to the subjects using felt-tip pens placed
approximately 2 cm in front of both nostrils, as follows. First, the
‘‘olfactory threshold’’ (T) is assessed using n-butanol presented by
means of a single staircase, using stepwise dilutions in a row of 16
felt tip pens. Second, ‘‘odor discrimination’’ (D) is assessed by
asking subjects to perform a triple forced choice using 16 pairs of
odorant. Third, ‘‘odor identification’’ (I) is assessed by asking
subjects to identify 16 individual odors by performing a forced
choice from a list of four verbal descriptors. Olfactory threshold
(T), discrimination (D) and identification (I) scores were then
added together to give the TDI score [57,58]. All subjects were
considered as normosmic according to the Sniffin’ Sticks test (TDI
scores ranging from 33 to 40.25; 37.262.16).
Stimuli
Chemosensory stimuli were produced by an air-dilution
olfactometer (OM2S, Burghart Medical Technology, Wedel,
Germany). The device is able to deliver brief pulses of odorant
embedded within a constant airflow. The rapid switching between
the odor and the control airflow is based on a vacuum line. During
stimulation, the airflow (8 l/min), temperature (36uC) and
humidity (80% relative humidity) remain strictly unchanged, thus
avoiding any concomitant stimulation of mechanical or heat
sensitive trigeminal receptors. Trigeminal stimuli were generated
by gaseous CO
2
(55% v/v) used to activate trigeminal afferents,
and olfactory stimuli were generated by 2-Phenylethanol (50% v/
v) used to activate olfactory afferents [5,59]. The stimuli were
delivered through a Teflon
TM
tube placed in the right nostril, just
behind the nasal valve, pointing towards the olfactory cleft.
Stimulus duration was 200 ms, with a rising time of 20 ms.
Procedure
Before the experiment, subjects were familiarized with the
experimental surrounding, the material used for the psychophys-
ical assessment of olfaction, as well as the olfactory and trigeminal
stimuli used to elicit the chemosensory ERPs. During the
experiment, olfactory and trigeminal stimuli were presented in
alternation. Each type of stimulus was repeated 60 times. Inter-
stimulus time interval between each stimulus was 15 s. Hence, the
inter-stimulus interval between two stimuli of the same type was
30 s. Subjects were instructed to breathe through the mouth and
to perform velo-pharyngeal closure to avoid any respiratory
airflow in the nasal cavity during stimulus presentation [60].
Subjects were also instructed to keep their eyes open during the
recording.
Electroencephalographic recording
The electroencephalogram was recorded continuously from 64
Ag/AgCl electrodes placed on the scalp according to the
International 10/10 system (Waveguard64 cap, Cephalon A/S,
Denmark). Scalp signals were recorded using an average reference.
Ocular movements and eye-blinks were recorded using two
additional bipolar surface electrodes placed at the upper-left and
lower-right sides of the left eye. Impedance was kept below
5 kOhm. Signals were amplified and digitized at a 1000 Hz
sampling rate (64-channel ASA-LAB EEG system, Advanced
Neuro Technologies, The Netherlands).
Data preprocessing
All EEG processing steps were carried out using BV Analyzer
1.05 (Brain Products, Germany), Letswave (http://nocions.
webnode.com/letswave) [25] and EEGLAB (http://sccn.ucsd.
edu/eeglab).
After referencing to the left (M1) and right (M2) mastoids, and
after band-pass filtering using a 0.3 to 30 Hz Butterworth zero
phase filter, the continuous EEG recordings were segmented into
2.0 s long EEG epochs ranging from 20.5 to +1.5 s relative to
stimulus onset.
After baseline correction (reference interval: 20.5 to 0 s), an
Independent Component Analysis (ICA) was performed to remove
electro-ocular artifacts, using the runica algorithm as implemented
in EEGLAB [61,62]. Artifact-free epochs were generated by
removing the ICs capturing clear electro-ocular artifacts (time
course typical of eye blinks, frontal scalp topography) [63,64].
Finally, epochs with amplitude values exceeding6100 mV (i.e.
epochs likely to be contaminated by an artefact) were rejected
(22611% of the total number of epochs).
Across-trial averaging in the time domain
For each subject, separate average waveforms were computed
for trigeminal and olfactory stimulation. Within these average
waveforms, two distinct peaks were measured at electrode Cz, as
described in previous studies [5,11,59,65,66]. For trigeminal
chemosensory ERPs, N1 was defined as the most negative peak
between 320 and 450 ms (TRI-N1) and P2 was defined as the
most positive peak between 450 and 800 ms (TRI-P2). For
olfactory ERPs, N1 was defined as the most negative peak between
320 and 450 ms (OLF-N1) and P2 was defined as the most positive
peak between 450 and 800 ms (OLF-P2). Peak latencies were
expressed relative to stimulus onset. Peak amplitudes were
expressed relative to baseline.
Across-trial averaging in the time-frequency domain
A time-frequency (TF) representation based on the continuous
Morlet wavelet transform (CWT) of EEG epochs was used to
characterize the amplitude of oscillatory activity as a function of
time and frequency. The Morlet wavelet consists in a complex
exponential function localized in time by a Gaussian envelope.
The initial spread of the Gaussian wavelet was set to 2.5/pv0(v0
being the central frequency of the wavelet; see also [25,37]).
Explored frequencies ranged from 0.3 to 30 Hz in steps of 0.3 Hz.
To obtain a time-frequency representation of trigeminal and
olfactory ERPs, the time-frequency transform was first applied to
the single-subject ERP waveforms obtained after time-domain
averaging (CWT-AVERAGE). Because time-domain averaging
cancels out all signal changes that are not strictly stationary across
trials, this transform revealed only stimulus-induced EEG changes
that were phase-locked to the stimulus onset (i.e. ERPs).
To obtain a time-frequency representation of both phase-locked
and non phase-locked EEG responses to trigeminal and olfactory
stimulation, the time-frequency transform was then applied to
each single EEG epoch (CWT-SINGLE). For each subject and
stimulus type, single-trial TF maps expressing signal amplitude
were then averaged across trials. Because this approach yields a
time-frequency map of the average oscillation amplitude regard-
less of phase, it enhanced both phase-locked (i.e. ERPs) and non
phase-locked (i.e. ERD, ERS and ERPs affected by a significant
amount of latency jitter) stimulus-induced changes in EEG
oscillation amplitude.
For each estimated frequency, CWT-AVERAGE and CWT-
SINGLE time-frequency maps were expressed relative to baseline
(pre-stimulus interval ranging from 20.4 to 20.1 s relative to
Time-Frequency Analysis of Chemosensory ERPs
PLoS ONE | www.plosone.org 9 March 2012 | Volume 7 | Issue 3 | e33221
stimulus onset), as follows: ER%
t,f
=(A
t,f
2R
f
)/R
f
, where A
t,f
is the
signal amplitude at a given latency tand frequency f, and R
f
is the
signal amplitude at the frequency f, averaged within the pre-
stimulus reference interval.
To assess the significance of the relative increases and decreases
of signal amplitude observed in the group-level average time-
frequency maps, for each time-frequency bin, a one-sample t-test
against zero was performed using the amplitudes estimated in each
subject. This yielded, for each type of stimulus, a time-frequency
map highlighting the regions where the EEG signal deviated
significantly from baseline (p,0.05). These statistical maps were
then used to define a number of regions of interest (ROI),
circumscribing stimulus-induced EEG responses. For each subject,
maximum or minimum amplitude values within each ROI were
used as summary values estimating response magnitude.
Discrimination performance
The aim of the present study was to characterize the phase-
locked and non phase-locked EEG responses to olfactory and
trigeminal stimulation and, most importantly, to evaluate the
reproducibility and robustness of these responses when recorded at
individual level, in order to assess their clinical usefulness.
For this purpose, an additional dataset was created by
segmenting the original continuous EEG recordings from 22.5
to 20.5 s relative to stimulus onset. Whereas the original dataset
(STIM) was expected to contain EEG responses triggered by the
olfactory or trigeminal stimulus, this additional dataset (NOSTIM)
was expected to contain only background EEG activity. Exactly
the same analysis was applied to this NOSTIM dataset. For each
stimulus type, Receiver Operating Characteristic (ROC) curves
were then constructed to examine and compare the ability of each
different measures of the magnitude of the EEG responses to
chemosensory stimulation to discriminate between STIM and
NOSTIM epochs, i.e. to discriminate between the stimulus-evoked
EEG responses and background noise. These analyses were
performed using MedCalc v. 11.5. (MedCalc Software, Belgium).
The area under the ROC curve (AUC) was used as an index of
discriminatory performance. An AUC of 0.5 would indicate
random performance, whereas an AUC of 0 and 1 would denote
perfect performance. For each measure, to assess the ability to
distinguish between presence vs. absence of a response, it was thus
examined whether the AUC was significantly different from 0.5
[67,68]. When significant (p,0.05, uncorrected for multiple
comparisons), the cut-off value (J) associated with the greatest
Youden index (Y = sensitivity+specificity-1) was chosen as decision
criterion [69,70]. This cutoff value corresponds to the point on the
ROC curve that is farthest from the diagonal line. Finally, to
compare the discrimination performance of the different EEG
measures, obtained ROC curves were compared using the
nonparametric approach for comparing the areas under two or
more correlated ROC curves described by Delong et al. [71].
To examine the involvement of the different EEG responses to
olfactory and trigeminal stimulation in chemosensory perception,
the relationship between their magnitude and the psychophysical
TDI scores assessing olfactory performance was assessed using the
Pearson’s correlation coefficient.
Author Contributions
Conceived and designed the experiments: CH TH PR AM. Performed the
experiments: CH AM. Analyzed the data: CH AM. Contributed reagents/
materials/analysis tools: CH TH AM. Wrote the paper: CH VL TH PR
AM.
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Time-Frequency Analysis of Chemosensory ERPs
PLoS ONE | www.plosone.org 11 March 2012 | Volume 7 | Issue 3 | e33221
