Objective Assessment of Spectral Ripple Discrimination
in Cochlear Implant Listeners Using Cortical Evoked
Responses to an Oddball Paradigm
Alejandro Lopez Valdes1*, Myles Mc Laughlin1,2, Laura Viani3, Peter Walshe3, Jaclyn Smith3, FanGang Zeng2, Richard B. Reilly1
1 Trinity Centre for Bioengineering, Trinity College, Dublin, Ireland, 2 Hearing and Speech Laboratory, University of California Irvine, Irvine, California, United States of
America, 3 National Cochlear Implant Programme, Beaumont Hospital, Dublin, Ireland
Abstract
Cochlear implants (CIs) can partially restore functional hearing in deaf individuals. However, multiple factors affect CI
listener’s speech perception, resulting in large performance differences. Non-speech based tests, such as spectral ripple
discrimination, measure acoustic processing capabilities that are highly correlated with speech perception. Currently
spectral ripple discrimination is measured using standard psychoacoustic methods, which require attentive listening and
active response that can be difficult or even impossible in special patient populations. Here, a completely objective cortical
evoked potential based method is developed and validated to assess spectral ripple discrimination in CI listeners. In 19 CI
listeners, using an oddball paradigm, cortical evoked potential responses to standard and inverted spectrally rippled stimuli
were measured. In the same subjects, psychoacoustic spectral ripple discrimination thresholds were also measured. A neural
discrimination threshold was determined by systematically increasing the number of ripples per octave and determining the
point at which there was no longer a significant difference between the evoked potential response to the standard and
inverted stimuli. A correlation was found between the neural and the psychoacoustic discrimination thresholds (R2 = 0.60,
p,0.01). This method can objectively assess CI spectral resolution performance, providing a potential tool for the evaluation
and follow-up of CI listeners who have difficulty performing psychoacoustic tests, such as pediatric or new users.
Citation: Lopez Valdes A, Laughlin MM, Viani L, Walshe P, Smith J, et al. (2014) Objective Assessment of Spectral Ripple Discrimination in Cochlear Implant
Listeners Using Cortical Evoked Responses to an Oddball Paradigm. PLoS ONE 9(3): e90044. doi:10.1371/journal.pone.0090044
Editor: Jyrki Ahveninen, Harvard Medical School/Massachusetts General Hospital, United States of America
Received August 23, 2013; Accepted January 28, 2014; Published March 5, 2014
Copyright: ß 2014 Lopez Valdes 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: This work was supported by a Higher Education Authority (HEA) Graduate Research Education Program in Engineering (GREP-Eng) scholarship to
Alejandro Lopez Valdes, an EU FP7 Marie Curie International Outgoing Fellowship to Myles Mc Laughlin and the National Institutes of Health, the United States
Department of Health and Human Services (P30 DC008369). 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: lopezvaa@tcd.ie
speech recognition (i.e. spectral resolution). A spectral ripple
discrimination test is a non-linguistic psychoacoustic method for
probing a normal hearing listener’s spectral resolution [15]. A
number of studies have now shown that spectral ripple discrimination correlates with different aspects of speech perception and
music perception in CI users [13,14,16,17].
To measure spectral ripple discrimination thresholds in CI
listeners, standard psychoacoustic threshold tracking methods are
normally employed. CI listeners actively listen to a number of
intervals containing either a standard stimulus or its ripple-phase
inverted counterpart. They are requested to report which interval
contained the inverted stimulus by, for example, pressing a button
corresponding to the interval. This approach produces reliable
results in adults. Although experienced researchers might be able
to use an observer based psychoacoustic procedure to measure
spectral ripple discrimination thresholds in infants [18], these
standard psychophysical approaches are difficult to apply to
special populations such as pediatric, pre-lingually deafened or
non-compliant users in clinical practice.
An alternative to psychoacoustic methods is to employ an
objective neural response to predict behavioral outcomes. An
Introduction
A cochlear implant (CI) can partially restore hearing in deaf
individuals, allowing most listeners to obtain 70–80% correct
sentence perception in quiet [1]. A CI is now the standard
treatment for severe to profound deafness worldwide, with infants
as young as 6 months being considered for implantation [2]. In
spite of this success, there remains a large amount of variability in
speech perception outcomes among CI listeners. While factors
such as duration of deafness, age at onset of deafness or duration of
CI use affect performance [3,4], they cannot completely account
for all the observed variability [5–10]. Factors such as temporal
and spectral processing capabilities also contribute to speech
perception outcomes [11–14]. To help understand the causes of
this performance variability, and to improve clinical evaluation
and follow-up of CI listeners, there is a need for tests which can
objectively quantify performance in both pediatric and adult
populations.
Standardized sentence and word recognition tests are useful for
directly measuring speech perception in CI listeners. However,
they cannot be used with pre-lingual children (a rapidly expanding
user group), nor do they directly asses underlying mechanisms of
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50 ms on and off cosine squared ramps were applied. Stimuli were
filtered with a long-term, speech-shaped filter [34]. All stimuli
were generated in MATLAB (MathWorks, Natick, MA) at
44.1 kHz and presented via a standard PC soundcard.
For both the psychoacoustic and evoked potential testing,
stimuli were presented via the audio line-in on the CI at the most
comfortable level, determined for each subject using a 0 (silence) to
10 (too loud) loudness scale, with 6 being the most comfortable
level. To limit the effects of any unwanted background noise the
CI microphone volume and sensitivity were set to the minimum
allowable values. Subjects used their everyday speech processing
strategy without any special adjustments other than changes to the
microphone volume and sensitivity. Stimuli were always presented
monaurally.
Psychoacoustic Procedure. A two-down, one-up, threealternative forced-choice [35] paradigm was used to track the
psychoacoustic spectral ripple discrimination threshold. Within
one trial, two of the intervals were randomly selected to present the
standard stimulus whilst the remaining interval presented the
inverted stimulus, with all three intervals having stimuli with the
same number of ripples/octave. If the subject’s spectral resolution
is sharp enough to resolve the spectral peaks and valleys, they
should hear a difference in the standard and inverted stimuli
[14,17]. The subject was asked to select the interval which was
different by pressing a button on a graphical interface. After two
consecutive correct responses, the number of ripples/octave was
increased by a ratio of 1.414. As the number of ripples/octave
increased the standard and inverted stimuli began to sound more
similar. After one incorrect response the number of ripples/octave
was decreased to the previously tested value. A run was terminated
after 13 reversals. The psychoacoustic spectral ripple discrimination threshold was defined as the mean of the last eight reversals
on the three-alternative forced-choice threshold tracking function
[35]. All subjects completed at least five repetitions of the test to
minimize any learning or attention effects. The final threshold was
taken as the mean of all completed tests.
advantage of this approach is that listeners do not need to respond
to the stimuli and often need not attend to the stimuli. Neural
responses from the auditory nerve and brainstem in CI listeners
have been shown to correlate reasonably well with threshold and
comfort stimulation levels [19–22], while cortical evoked potentials
have been shown to correlate with higher level outcomes such as
speech perception [23–26], musical perception and auditory
plasticity [27–32]. In particular, mismatch negativity (MMN)
responses have been proposed as an objective index of auditory
discrimination for different clinical conditions [33]. The MMN
response can be obtained, via an unattended oddball paradigm, as
the evoked potential difference between a frequently presented
stimulus (standard) and a less frequently and randomly presented
different stimulus (deviant or oddball).
The aim of this study was to use an unattended oddball
paradigm to develop and validate a completely objective method
for measuring spectral ripple discrimination thresholds in adult CI
listeners. An objective method for measuring spectral ripple
discrimination thresholds would potentially provide an additional
tool when standard psychophysical approaches are difficult to
apply to certain CI populations.
Materials and Methods
Subjects and Ethics Statement
Subjects. Nineteen adult CI listeners (6 male, 13 female)
participated in the present study at two separate locations: Trinity
Centre for Bioengineering, Trinity College Dublin (n = 15) and
Hearing and Speech Laboratory, University of California Irvine
(n = 4). One bilateral subject was evaluated separately for both ears
yielding a total of 20 ears tested. Exclusion criteria applied to
subjects under 18 years of age and subjects with cognitive or
learning disabilities. There were no subjects withdrawn from this
study. Subjects were aged between 31 and 79 years (mean 56,
standard deviation 15). They used either a Cochlear (n = 17), MedEl (n = 1) or Advanced Bionics (n = 1) implants (device details on
implant type and usage experience are shown in Table 1). All
subjects used monopolar stimulation mode.
Evoked Potential Methods
Evoked Potential Stimuli. The stimuli used in the evoked
potential paradigm were similar to those used in the psychoacoustic paradigm except that 4000 pure tones ranging from 100 to
8000 Hz were used to cover the full frequency range of the CI
filter bank. The lower pure tone range in the psychoacoustic
stimuli allowed for the stimuli to be generated and presented faster
while still presenting some energy to the highest CI high-frequency
band.
Standard and ripple phase-inverted stimuli with durations of
either 300 or 500 ms and with 0.125, 0.25, 0.5, 1, 2, 4 and 8
ripples/octave were generated and stored. Examples of the stimuli
characterization at one and four ripples/octave can be seen in
Fig. 1. There was no significant difference for the use of 300 or
500 ms stimuli with respect to the CI artifact, therefore, data from
both stimuli duration were pooled together for analysis. The same
set of stored stimulus tokens were used for all presentations to all
subjects. In Trinity College Dublin stimuli were presented via a
standard PC soundcard (44.1 kHz sampling rate) and in University of California, Irvine stimuli were presented using a USB digital
to analog converter (DAC, 44.1 kHz sampling rate) (NI-USB
6221, National Instruments, Austin, TX).
Evoked Potential Acquisition. Fig. 2 shows a wideband,
high-sampling rate, acquisition system that uses single-channel
artifact attenuation to record late auditory evoked potentials in
response to the spectral ripple stimuli presented in an oddball
paradigm. The setup, along with the artifact attenuation
Ethics Statement
Experimental procedures were approved by the Ethics (Medical
Research) Committee at Beaumont Hospital, Beaumont, Dublin,
the Ethical Review Board at Trinity College Dublin and The
University of California Irvine’s Institutional Review Board.
Written informed consent was obtained from all subjects.
Psychoacoustic Methods
Psychoacoustic Stimuli. Psychoacoustic spectral ripple discrimination thresholds were determined in all subjects using
stimuli similar to that employed by Won et al. [14]. Stimuli were
generated by summing 250 pure tones ranging from 250 to
5000 Hz. The amplitudes of the pure tones were determined by a
full-wave rectified sinusoidal envelope on a logarithmic amplitude
scale. The ripple peaks were spaced equally on a logarithmic
frequency scale. The starting phases of the components were
randomized for each presentation. The ripple stimuli were
generated with 14 different densities, measured in ripples/octave.
The ripple densities differed by ratios of 1.414 (0.125, 0.176,
0.250, 0.354, 0.500, 0.707, 1.000, 1.414, 2.000, 2.828, 4.000,
5.657, 8.000, and 11.314 ripples/octave). Standard and ripplephase inverted stimuli were generated de novo in each trial run.
For standard stimuli, the phase of the full-wave rectified sinusoidal
spectral envelope was set to zero radians, and for phase-inverted
stimuli, it was set to p/2. The stimuli were 500 ms in duration and
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Table 1. Psychoacoustic and neural discrimination thresholds.
Subject ID
Implant
Tested Ear
Years of CI Experience
Psychoacoustic Spectral Ripple Discrimination
Threshold (RPO)
Neural Spectral Ripple Discrimination Threshold (RPO)
Positive Area
Negative Area
Total Area
0.434
3
UCI 01
Maestro
Left
2
0.574
0.420
0.398
UCI 02
Freedom
Right
5
1.403
1.008
0.900
0.989
UCI 03
Freedom
Left
9
2.210
3.202
6.335
5.974
UCI 03
Freedom
Right
1
1.542
2.085
1.188
2.407
UCI 04
Freedom
Right
4
2.595
1.252
0.659
1.045
TCD 01
Freedom
Left
3.5
1.158
1.793
0.665
0.763
TCD 02
Freedom
Left
4
0.381
0.193
0.337
0.225
TCD 03
Clarion 1.2
Right
12
0.948
0.909
0.589
0.953
TCD 04
Freedom
Left
7
0.618
0.248
0.221
0.237
TCD 05
Freedom
Left
5
2.172
2.957
2.861
2.987
TCD 06
Freedom
Right
1
0.658
*
*
*
TCD 07
Freedom
Right
1
0.778
*
0.161
0.150
TCD 08
CI512
Right
1
0.400
0.473
0.239
0.409
TCD 09
Freedom
Left
3.5
0.235
0.176
*
0.138
TCD 10
Freedom
Right
3.5
0.312
0.821
0.546
0.739
TCD 11
CI24RE
Right
9
1.113
0.489
0.833
0.782
TCD 12
Freedom
Right
4
0.931
1.618
1.497
1.597
TCD 13
CI24R
Left
8
0.463
*
0.461
0.482
TCD 14
CI24M
Right
12
1.503
1.717
1.870
1.827
TCD 15
Freedom
Left
5
0.240
*
*
*
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RPO- Ripples per octave.
* Unable to estimate a neural spectral ripple discrimination threshold.
doi:10.1371/journal.pone.0090044.t001
Objective Assessment of CI Spectral Resolution
ADC and used to synchronize stimulus presentation and
acquisition.
Evoked Potential Procedure. Standard and ripple phaseinverted stimuli with the same number of ripples/octave were
presented in an unattended oddball paradigm. The deviant
stimulus was the ripple phase-inverted stimulus, having an
occurrence probability of 10%, and the standard stimulus was
the non-inverted stimulus. The inter-onset interval for each
stimulus presentation was one second. One run began with 20
presentations of the standard stimulus after which the deviant
randomly occurred at least once in every 10 stimulus presentations, with the additional condition that a deviant was never to be
followed by another deviant. The paradigm was repeated at least
four times for every subject, each time using stimuli with a
different number of ripples/octave. Subjects were instructed to
ignore the stimulus and to minimize movement to avoid
movement artifacts in the recordings. Each oddball paradigm
lasted approximately 12–15 minutes. Subjects watched a silent
captioned film and rest breaks were provided after each run or
upon subject’s request. EEG data collection lasted approximately
one hour per subject. At Trinity Centre for Bioengineering the
acquisition sessions took place in a dedicated EEG room, while at
the University of California Irvine, the sessions took place in a
sound booth.
Evoked Potential Epoching. Raw EEG data were segmented into long epochs of 1100 ms, 300 ms pre- to 800 ms poststimulus onset to avoid filter edge affects. Shorter epochs of
100 ms pre- to 500 ms post-stimulus were used for plotting the
data. A baseline correction of 150 ms pre-stimulus was applied in
all filtered epochs. Epochs were classified as response to standard
or deviant stimuli and averaged across presentations. Online
averaging and artifact attenuation allow the real time display of
the evoked potential response to both standard and deviant
stimuli. To speed up data collection a run was terminated when
collecting more deviant responses did not significantly change the
shape of the averaged deviant waveform. This change was
evaluated by measuring the sum of squared differences of the
averaged deviant epochs every time a new epoch was included,
when the sum of squared differences stabilized at a low value it was
determined that no significant change would be produced with the
addition of more epochs. This was typically once 60 or 70 deviant
responses were acquired, with a minimum of 50 deviants per run
always being collected. A difference (or mismatch) waveform was
calculated by subtracting the response to the standard stimuli from
the response to the deviant stimuli. The oddball paradigm was
repeated using stimuli with different numbers of ripples/octave,
yielding one difference waveform for each ripple/octave stimulus.
Evoked Potential Artifact Attenuation. Mc Laughlin et al.
[36] showed that with the wideband, high sampling-rate acquisition system, the CI related artifact consists of two components: a
high frequency component which is a direct representation of the
stimulation pulses and a low frequency component which is related
to the envelope of the stimulation pulses. A 2nd order Butterworth
band-pass filter (2–20 Hz, 12 dB/octave slope) was applied to the
averaged standard and deviant responses (Fig. 3A and B single
responses before filtering, Fig. 3C and D averaged responses after
filtering). The low-pass edge of this filter attenuated the high
frequency artifact component and the high-pass edge removed
drift. The filter was applied using a zero-phase forward and reverse
digital filtering method (filtfilt command, MATLAB).
It was observed that, within a subject, the signal envelopes
derived from the CI stimulation pulse sequence associated to the
standard and deviant stimuli were similar (compare Fig. 3A and
B). A cross-correlation of 112 sets of standard and deviant CI
Figure 1. Stimuli characterization. (A) Frequency spectrum of a
500 ms standard stimulus with spectral peak density of one ripple per
octave (RPO). Stimuli were composed of the sum of pure tones in a
range of 0.25–5 kHz (psychoacoustic) or 0.1–8 kHz (electrophysiology).
Spectral amplitudes were defined by a full-wave rectified sinusoidal
envelope. One spectral peak can be clearly distinguished at the 0.5–
1 kHz octave. Peak to valley amplitude of 30 dB as well as the high
frequency attenuation of the speech-shaped filter can also be seen. (B)
Spectrogram of the standard stimulus described, showing the
frequency content of the stimulus along the 500 ms duration. Spectral
peak density of one RPO can clearly be resolved in the 4–8 kHz octave.
(C) Frequency spectrum of the corresponding phase-inverted, or
deviant, stimulus employed along with the standard stimulus at one
RPO in an oddball paradigm. The spectral envelope is shifted by p/2
with respect to the standard stimulus, as observed in the 0.5–1 kHz
octave. (D) Spectrogram of the deviant stimulus, showing the inversed
frequency content along the 500 ms duration with respect to the
standard stimulus. (E) Frequency spectrum of a standard stimulus with
spectral peak density of four RPO showing the increased spectral
density with respect to the one RPO stimuli. (F) Spectrogram of the
standard stimulus at four RPO. Spectral peak density of four RPO can
clearly be resolved in the 4–8 kHz octave.
doi:10.1371/journal.pone.0090044.g001
procedure, is described in detail elsewhere [36]. Briefly, the
sampling rate on the analog to digital converter (ADC) (NI-USB
6221, National Instruments, Austin, TX) was set to 125 kHz, the
amplifier (SRS 560, Stanford Research Systems, Sunnyvale, CA)
gain was set to 2000, the amplifier high-pass filter was set to 0.03
and the low-pass filter to 100 kHz. Standard gold cup surface
electrodes were placed at Cz, on the mastoid and on the
collarbone, these last two electrodes were placed contralateral to
the CI location. The positive end of the amplifier was connected to
Cz, the negative end to the mastoid and the ground to the
collarbone. Electrode impedances were always below 5 kV and
care was taken to ensure that impedances were matched to within
1 kV to minimize low frequency artifacts [36]. The output of the
amplifier was connected to one channel on the ADC. A trigger
pulse generated simultaneously with the stimulus, and presented
on a separate channel, was connected to a second channel on the
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Figure 2. Single-channel acquisition set-up. Single-channel EEG acquisition system, featuring wideband and high-sampling rate recordings. EEG
is recorded from electrode position Cz, referenced to the mastoid contralateral to the tested ear and grounded on the collar bone. The EEG signal is
amplified with a biological differential pre-amplifier (SR560, Stanford Research System, Sunnyvale, CA) with filter settings at 0.03 Hz and 100 kHz. The
signal is then digitized with an ADC (NI-USB 6221, National Instruments, Austin, TX) sampled at 125 kHz and recorded with a custom made software
made in MATLAB (The MathWorks, Natick, MA). Stimulus and trigger presentation is done through the sound card of the computer. The trigger is fed
to the ADC for event synchronization and the stimulus is presented via a personal audio cable to the auxiliary port of the subject’s speech processor.
doi:10.1371/journal.pone.0090044.g002
attenuated any low frequency artifact components, leaving a
difference waveform dominated by neural response (Fig. 3E).
stimulation pulse sequences supported this observation (mean
normalized correlation = 0.8871, standard deviation = 0.1597).
With the result that any low frequency artifact component was
equally present in both the response to the standard and the
response to the deviant (compare onset and offset artifacts in
Fig. 3C and D), calculating the difference waveform adequately
Figure 3. Artifact attenuation and evoked potential extraction. (A)–(B) Single EEG acquisition epoch of a 500 ms stimulus presented to a CI
user. Data acquisition at a high-sampling rate (125 kHz) allows for the CI artifact to be clearly resolved from the recorded data as a high frequency
and large amplitude component present during the 500 ms of stimulus duration (standard in black, deviant in blue). (C)–(D) Applying a 2nd order
Butterworth band-pass filter (2–20 Hz) to the averaged epochs, recorded from an oddball paradigm, it is possible to attenuate the CI artifact and
extract the evoked potential (EP) elicited to the each stimulus type (standard in black, deviant in blue). The N100, characteristic of auditory EPs can be
identified in both standard (C) and deviant (D) stimuli types as a negative peak at around 100–150 ms. In some cases, after filtering, a low-frequency
artifact is present at stimulus onset and offset with similar shape and amplitude in both standard and deviant responses. (E) A difference waveform is
calculated by subtracting the neural response elicited to the standard stimuli from the neural response elicited to the deviant stimuli. This method
allows further attenuation of residual low-frequency artifacts.
doi:10.1371/journal.pone.0090044.g003
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Evoked Potentials: Spectral Ripple Discrimination
Thresholds
Results
Psychoacoustic Spectral Discrimination Thresholds
If a listener
can acoustically perceive a difference between a standard and
deviant stimulus, the evoked potential response to the deviant
stimulus, when presented in an oddball paradigm, will differ in
shape from that evoked by the standard stimulus [33,37]. This
response is normally quantified by calculating a difference
waveform, i.e. deviant response minus standard response and is
often referred to as mismatch negativity. If the standard and
deviant responses are the same, the difference waveform should be
flat; while if they differ, the difference waveform will show
oscillations. In practice the noise inherent in evoked potential
recordings means that even if the underlying standard and deviant
waveforms are identical the difference waveform will still show
some oscillations. Therefore, to calculate a neural discrimination
threshold it was necessary to first quantify the amount of noise in
the difference waveform and then define what quantifies a
significant difference waveform response.
Calculating the Difference Waveform Noise Floor. The
noise present in one difference waveform was calculated by
applying a bootstrap method to all the standard responses
collected for that subject during that run. A randomly chosen
sub-sample of 10% of all standard responses was chosen and
averaged together to create a bootstrapped deviant response
(Fig. 4A, blue line). The remaining 90% of the standard stimuli
was then averaged together to create bootstrapped standard
response (Fig. 4A, black line). The bootstrapped deviant was
subtracted from the bootstrapped standard to give a bootstrapped
difference waveform (Fig. 4B, red line). If no noise were to be
present in the recording this bootstrapped difference waveform
would be completely flat. Thus, oscillations present in the
bootstrapped difference waveform quantify the noise present in
the recording. The bootstrap procedure was repeated to generate
54 separate bootstrapped difference waveforms. The noise floor
was defined as the standard deviation of all bootstrapped
difference waveforms at each time point for positive and negative
values (Fig. 4B, black lines).
Hypothesis and Methodological Overview.
Table 1 summarizes the individual spectral ripple discrimination thresholds for all ears tested. The range (0.235 to 2.595
ripples/octave) and mean (1.012 ripples/octave) are in general
agreement with previously reported values for spectral ripple
discrimination in CI listeners [13,14,38].
Evoked Potential Spectral Ripple Discrimination
Thresholds
Evoked Potentials and Difference Waveform. Fig. 5A
shows an example of evoked potential waveforms recorded using
an oddball paradigm in response to a 0.25 ripples/octave stimuli.
The black line shows the response to the standard (standard
spectral ripple stimulus) and the blue line the response to the
deviant (inverted spectral ripple stimulus). This user reported
hearing a difference between the standard and the deviant
stimulus (psychoacoustic spectral ripple discrimination threshold
of 2.210 ripples/octave) and correspondingly there was a marked
difference in the response to the deviant. The deviant response has
larger amplitude P2 than the standard response. It also contains a
N3 and P4 component which are not present in the standard
response. Fig. 5B shows the difference waveform calculated by
subtracting the standard from the deviant response. The P2, N3
and P4 differences are apparent in the difference waveform and,
importantly, their peaks are above or below the noise-floor
indicating that the neural response to the deviant is significantly
different than the neural response to the standard.
To determine a neural spectral ripple discrimination threshold,
responses to spectral ripple stimuli with an increasing number of
ripples/octave were measured in all subjects. The standard and
deviant responses for one subject to stimuli with 0.25, 0.5, 1 and 2
ripples/octave are shown in Fig. 6A. The large positivity, around
40 ms, present in all standard and deviant responses is probably an
onset artifact. The standard responses to the 0.25, 0.5, 1 and 2
ripples/octave stimuli are similar. However, the deviant responses
change as the number of ripples/octave is increased. The deviant
response to the 0.25 ripples/octave stimulus shows a large increase
in the N1 and P2 component when compared with the standard
response. As the number of ripples/octave increases (and the
stimuli begin to sound more alike) this N1-P2 difference becomes
smaller and delayed, until at 2 ripples per octave the response to
the deviant is essentially the same as the response to the standard.
This subject had a psychoacoustic spectral ripple discrimination
threshold of 1.503 ripples/octave. Fig. 6B shows the difference
waveforms. Since the onset artifact (around 40 ms) was equally
present in both standard and deviant responses it is significantly
attenuated in the difference waveform. Calculating the area above
and below the noise floor (shaded) within a 90–450 ms time
window allows a quantification of the difference in the neural
response to the standard and deviant stimuli. This area is large for
0.25 ripples/octave where the subject perceives a clear difference
between the standard and deviant stimuli and is negligible at 2
ripples/octave where the subject reports that the standard and
deviant stimuli sound the same.
Defining a Significance Level. In Fig. 7, the area above and
below the noise floor, and the total area, are plotted as a function
of ripples/octave for the same subject shown in Fig. 6. It is clear
that as the number of ripples/octave increases, the area above and
below the noise floor decreases, i.e., the standard and deviant
responses become similar. To allow the objective estimation of
neural spectral ripple discrimination thresholds, a significance level
(i.e. an area in microvolt times millisecond ‘mVms’) was defined as
Defining
a
Significant
Difference
Waveform
Response. To quantify the difference waveform the area above
the noise floor within a 90 to 450 ms time window was calculated.
This time window allows for the expected evoked potential
components such as N1, P2, N2, P3 or MMN to be included in the
analysis. Given that the difference waveform is defined as
microvolts in function of time in milliseconds, the area above
the noise floor is defined as microvolts times milliseconds ‘mVms’.
A neural spectral ripple discrimination threshold was then defined
as the point at which this area dropped below a predetermined
significance level. As the aim of this study was to develop an
objective evoked potential test to accurately predict the psychoacoustic spectral ripple discrimination threshold, the significance
level was determined by calculating the neural threshold for a
range of different significance levels and selecting the significance
level which gave the best correlation with the psychoacoustic
threshold across all subjects. The ‘Defining a Significance Level’
section presents details of how this procedure was applied together
with results from a validation study where data from all 19 subjects
were randomly partitioned into two groups. One group was used
to estimate the significance level and the other group to test the
accuracy of this significance level by predicting the psychoacoustic
spectral ripple discrimination threshold.
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Objective Assessment of CI Spectral Resolution
Figure 4. Noise floor calculation of the neural response. (A) The noise floor was calculated with a statistical bootstrap method. A random 10%
sub-sample of epochs from the standard stimulus type was averaged to create a bootstrapped deviant response whilst the remaining epochs were
averaged together to create a bootstrapped standard response. (B) A difference waveform was calculated by subtracting the bootstrapped standard
response from the bootstrapped deviant response. This process was repeated 54 times, each time with a different randomly selected 10% sample of
standard epochs. The noise floor of the signal was defined as +/2 one standard deviation of the 54 resulting difference waveforms.
doi:10.1371/journal.pone.0090044.g004
the threshold below which area differences between the standard
and deviant response can be considered perceptually negligible.
A bootstrap method was employed to define and validate this
significance level for the three different area measurements. The
approach, described in detail below and in a flow chart in Fig. 8A,
Figure 5. Example of the difference waveform elicited using the oddball paradigm. (A) Evoked potential responses elicited to 608
standard stimuli and 65 deviant stimuli at 0.25 RPO. When the standard and deviant stimuli are perceived as different sounds, the morphology of the
neural response to the deviant stimuli (blue trace) is significantly different than the response to the standard stimuli (dashed, black trace). (B) The
difference waveform represents the mismatch between the responses elicited to each stimulus type.
doi:10.1371/journal.pone.0090044.g005
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Objective Assessment of CI Spectral Resolution
Figure 6. Sequential decrease of the difference waveform’s area above the noise floor. (A) Evoked potential traces of standard and
deviant stimuli elicited at 0.25, 0.5, 1 and 2 RPO. As the spectral density increases, the neural responses to the standard and deviant stimuli become
more similar. (B) The difference waveform at each spectral density shows a sequential decrease of the mismatch between the responses elicited to
each stimulus type. The area above the noise floor of the signal (shaded grey) is taken as an indicator of said mismatch decrease.
doi:10.1371/journal.pone.0090044.g006
p-values (Fig. 8B). Significance levels, for which the regression
yielded a p-value greater than 0.01 or which excluded more than
two datasets, were discarded. From the remaining significance
levels, the one that yielded the greatest regression R2 was selected
(Fig. 8B, red dot). The selected significance level was applied to the
estimation group to determine the neural spectral ripple discrimination threshold and then quantify, using linear regression, how
well this predicted the psychophysical threshold (Fig. 8C). If this
(estimation) regression yielded a p-value less than 0.05 with no
dataset exclusions then the significance level was accepted.
Otherwise, the significance level was rejected. One point on
Fig. 8C represents one of the accepted estimation R2 and p-values.
Fig. 8C shows the p-values as a function of the regression R2 value
for the estimation group’s linear regression.
This process was repeated, each time using a different random
partitioning of the datasets into determination and estimation
groups, until 20 significance levels that satisfied the criteria were
generated (Fig. 8D). This shows that the significance level chosen
performs accurately when estimating the psychoacoustic thresholds measured for each subject. The final significance levels
defined for this study (and employed in Section 3.4) was the
operated by first dividing the data into two groups. The first group
(a determination group) was employed to determine one significance level, for all members, which gave the best correlation with
the known psychophysical thresholds. The second group (an
estimation group) was then employed to test how well this
significance level could estimate the known psychophysical
threshold.
Data from the 20 tested ears were separated in two groups: a
determination and an estimation group. The determination group
contained 12 randomly selected datasets whilst the estimation
group contained the remaining 8 datasets. This partition ratio was
chosen so that the estimation group represented more than a third
of the total sample. Each dataset contained at least four
measurements presenting stimuli with different ripples/octave.
For the determination group, the neural spectral ripple discrimination threshold was calculated and linearly regressed with the
measured psychoacoustic threshold for each subject. If the area
never went below the significance level the dataset was excluded.
This regression was tested for a range of 19 different predetermined significance levels, ranging from 10 mVms to 100 mVms at
5 mVms increments, yielding 19 different (determination) R2 and
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Objective Assessment of CI Spectral Resolution
olds for the positive, negative and total area are reported in
Table 1.
Correlation between Psychoacoustic and Neural
Thresholds
Linear regression of the psychoacoustic spectral ripple discrimination thresholds with the neural spectral ripple discrimination
thresholds produced a squared Pearson’s correlation coefficient
(R2) of 0.60 and p-value,0.01 for total area (Fig. 9A), R2 = 0.65
and p-value,0.01 for the positive area (Fig. 9B), and R2 = 0.50
and a p-value,0.01 using the negative area (Fig. 9C).
Results from paired t-tests between psychoacoustic and neural
thresholds, in all three area measurements, show no significant
difference between the metrics: p-value = 0.75, t = 0.32 for positive
area; p-value = 0.93, t = 0.09 for negative area; and p-value = 0.68,
t = 20.41 for total area above the noise floor. A Steiger’s Z test
was employed to compare the correlations derived from the
positive, negative and total area calculations. Results indicate that
there is no significant difference between: the positive area and
negative area correlations (Z = 1.51, p-value.0.05); the positive
area and the total area correlations (Z = 1.14, p-value.0.05) and;
the negative area and the total area correlation (Z = 21.34, pvalue.0.05).
Discussion
Figure 7. Estimation of the spectral ripple discrimination
threshold based on neural responses. The neural spectral ripple
discrimination threshold is estimated as the point where the mismatch
between the neural responses dropped below a set significance level.
Thresholds were estimated with three different area above the noise
floor measurements: positive area, negative area and total area above
the noise floor.
doi:10.1371/journal.pone.0090044.g007
The present study developed and validated a method to
objectively assess spectral ripple discrimination in a population
of CI listeners using an oddball EEG paradigm. Using a clinically
applicable single channel set-up [36], it was possible to acquire
evoked potential responses to standard and deviant stimuli in CI
listeners. Analysis of the difference waveform showed a strong
correlation with behavioral spectral ripple discrimination thresholds, validating the utility of this approach as a clinical assessment
tool.
average of the accepted significance levels. The entire process was
repeated for the positive, negative and total area measurements.
For the total area the mean significance level was determined to
be 70.4 mVms (17.7 standard deviation). Two tested ears did not
yield a neural threshold (Fig. 9A). For one tested ear (TCD 06 in
Table 1) the area above the noise floor for all recordings was below
the significance level defined, and for the remaining exclusion
(TCD15 in Table 1), the area above the noise floor did not drop
below significance level. The mean neural threshold across 18
datasets was 1.230 ripples/octave (1.386 standard deviation).
For the positive area the mean significance level was determined
to be 36.3 mVms (13.8 standard deviation). Four datasets did not
yield a neural threshold using the positive area (Fig. 9B). The area
above the noise floor from two tested ears (TCD 13 and TCD 15
in Table 1) did not drop below the significance level in any of the
four recordings. Contrastingly, the area above the noise floor from
the remaining two exclusions (TCD 06 and TCD07 in Table 1)
was below the significance level in all four recordings. Hence, it
was not possible to estimate the neural spectral ripple discrimination threshold. The mean neural threshold for the remaining 16
datasets was 1.121 ripples/octave (0.920 standard deviation).
For the negative area the mean significance level was
determined to be 40 mVms (3.9 standard deviation). Three
datasets did not yield a neural threshold (Fig. 9C). The area
above the noise floor of three tested ears (TCD 06, TCD 09 and
TCD15 in Table 1) was below the significance level in every
recording, making it not possible to estimate a neural spectral
ripple discrimination threshold with the defined significance level.
The mean neural threshold across 17 datasets was 1.116 ripples/
octave (1.458 standard deviation). The individual neural thresh-
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Artifact removal
It was possible to distinguish the expected N1-P2 complex from
the evoked potential traces. The large positivity at around 40 ms
and negativity at around 500 ms after stimulus onset found in
some subjects (see Fig. 3C and Fig. 3D) are most likely on-set and
off-set artifacts caused by high-pass filtering of the low frequency
(or pedestal) artifact component identified by Mc Laughlin et al.
[36] and others [39–42] related to the CI’s response to the stimuli.
The 40 ms delay in the on-set artifact is caused by a combination
of the rise time of the stimuli (50 ms), the CI processor delay (,5
to 8 ms as observed in single stimulus presentations, see Fig. 3A
and Fig. 3B) and the high-pass filter characteristics. When present,
on-set and off-set artifacts where equally present in both standard
and deviant responses. Thus, the subtraction operation, employed
to obtain a difference waveform, attenuated these artifacts. The
analysis time window (90 to 450 ms) also minimized any potential
artifact influence on the area measurement used to determine the
neural spectral ripple discrimination threshold.
Objective assessment of neural thresholds
Judging the presence or absence of a neural response in cortical
evoked potentials (or in a difference waveform) is generally a
subjective task. This study developed and validated an objective
statistical approach to determine the point at which a response in
the difference waveform became perceptually non-significant.
Parts of this approach are similar to the integrated mismatch
negativity metric developed by Ponton et al. [43]. Measuring the
peak amplitude of specific components in the spectral ripple
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Objective Assessment of CI Spectral Resolution
Figure 8. Bootstrapped determination of the significance level. (A) Describes the progression of the bootstrapping method employed to
determine the level at which neural spectral ripple discrimination thresholds were estimated and regressed with the measured psychoacoustic
thresholds. (B) The square of the Pearson’s correlation factor (R2) vs. the 19 significant levels tested on the determination group is plotted. The
significance level that yields the maximum R2 value within the selection criteria, identified as the red point in the plot, is selected to continue with the
bootstrap method, the rest are excluded (hollow stars). (C) The selected significance level is evaluated with estimation group. The regression’s p-value
plotted vs. the regression’s R2 value resulting from the significance level evaluation on the estimation group for 20 bootstrap iterations. If the
evaluation yields no exclusions and a p-value less than 0.05, the significance level is stored. (D) The bootstrap method is repeated to select 20
different significant levels. The mean of the selected values is employed as the final significance level.
doi:10.1371/journal.pone.0090044.g008
(Fig. 8B), and most partitions of the data produce an estimate of
the significance level close to 70 mVms (Fig. 8D). This shows that
the good correlation between neural and psychophysical thresholds (Fig. 9) is robust and is not simply dependent on subjectively
selecting the appropriate significance level. The use of the positive,
negative or total area between the noise floor and the difference
waveform did not yield a significant difference when estimating
spectral discrimination thresholds. However, using the total area,
above the positive and negative noise floor, succeeded to estimate
a spectral ripple discrimination threshold in the largest number of
tested ears.
In cases where the cortical responses were too small compared
to the noise floor, such as TCD06 and TCD15, it was difficult to
estimate a neural spectral ripple discrimination threshold. While
monitoring the impedance levels accordingly during the recording
difference waveform is difficult because not all subject’s responses
exhibit a similar morphology (compare Figs. 5 and 6). The more
general approach taken in this study, of measuring the area above
or below a bootstrapped determined noise floor, avoids this
difficulty. An area-, as opposed to peak-, based metric has the
added advantage of reducing noise, an important consideration
when using difference waveforms which are by definition noisier
than the responses from which they are derived. To define a
significance level, below which a difference waveform area would
be considered perceptually insignificant, a second bootstrap
method was applied. Fig. 8C shows that, for 20 different data
partitions, the selected significance level reasonably predicts the
psychophysical thresholds of the estimation group. Additionally,
variations of the significance level between around 20 and
80 mVms do not tend to produce large variations in the R2 values
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Objective Assessment of CI Spectral Resolution
Potential Clinical Applications
Previous work by our group [36] highlighted the elegance of
single channel EEG acquisition and artifact attenuation in CI
users. The simple, yet robust, approach makes it feasible for use
within a clinical environment, with faster and more comfortable
acquisition than with high density EEG set-ups. The results
presented in this study suggest that the single channel EEG
acquisition and artifact attenuation is a reliable method for
measuring cortical responses to an oddball paradigm in CI users.
In addition to simply evaluating a CI subject’s spectral
resolution, it may also be possible to use the method to fine tune
a subject’s frequency map. Typically, a CI processor would be
loaded with a standard frequency map, i.e. predefined frequency
bands assigned to each electrode of the CI. An objective metric for
spectral resolution, such as the one presented in this study, could
allow the evaluation of customized frequency maps, in search of
the map that allows the best spectral resolution. The time required
to obtain spectral discrimination thresholds, approximately one
hour, is a limiting factor for this potential application. However,
being an objective metric, the possibility of an automated process
may reduce the number of man-hours required for the task.
Furthermore, the development of intra-cochlear recording methodologies that allow the recording of cortical evoked potentials
without the additional EEG systems [44] may benefit from
objective metrics as a building block for the development of
automated frequency tuning processes.
Current efforts to enhance spectral resolution via different
electrode stimulation modalities, i.e. partial bipolar stimulation
(pBP), tripolar stimulation (TP) and partial tripolar stimulation
(pTP), benefit from psychoacoustic evaluation of frequency
resolution [45–47]. Objective assessment of spectral resolution
using an oddball paradigm could be beneficial when evaluating
different electrode stimulation modalities in CI populations where
standard psychoacoustics cannot be performed such as young
infants. The use of an oddball paradigm such as the mismatch
negativity (MMN) has reported successful in normal hearing and
cochlear implant infant populations [48–50]. Evidence in literature suggests that the pitch discrimination characteristics of the
MMN in infants is developed between two and four months of age
[49].
Clinical applications involving the use of cortical evoked
potentials may be limited by the confounding factor of maturation
changes during the longitudinal development of cortical potentials.
The development of cortical auditory potentials can extend into
adolescence [50] and even after prolonged acoustic deprivation,
cortical auditory potentials can be re-developed over a period of
time [30]. Changes in the morphology, latency and amplitude of
potentials over time represents an impediment when performing a
within subject cortical evoked potential assessment. Trainor et al.
[51] identified changes in the EEG morphology of the MMN in
young infants, with an age range of two, three, four and six
months, suggesting that the difference at each age may be
associated with layer-specific maturational processes in auditory
cortex. However, the method developed in this study may
overcome these limitations due to the robust nature of the oddball
paradigm response and its applicability with different age
populations as well as clinical conditions [33,49,50]. Despite
maturational changes reflected by the EEG morphology of the
MMN in young infants, the cognitive change detection mechanism
associated with the MMN has been proposed to be developed
between two and four months of age [49].
Provided that a spectral ripple discrimination threshold could be
obtained with an oddball paradigm at any stage of the cortical
auditory potential maturation process, a within subject assessment
Figure 9. Correlation of neural and psychoacoustic spectral
ripple discrimination thresholds. Linear regression of the psychoacoustic spectral ripple discrimination thresholds with the neural
spectral ripple discrimination thresholds for each of the analyzed area
above the noise floor measurements: (A) total area above the noise
floor; (B) Positive area above the noise floor; and (C) negative area
above the noise floor.
doi:10.1371/journal.pone.0090044.g009
may reduce noise and CI artifact, small or unreliable cortical
evoked potential responses from some subjects is a limitation when
estimating neural spectral ripple discrimination thresholds.
Reducing the noise in the signal as much as possible by limiting
subject motion and external interference and increasing the
stimulus presentation level may help get a better response in these
subjects.
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Objective Assessment of CI Spectral Resolution
the applicability of the proposed objective method in a population
of infant CI recipients.
can be conducted regardless of the developmental changes
presented during the duration of the assessment. Nonetheless,
determining the applicability of spectral rippled stimuli as well as
the complexity of the paradigms and the presentation rate for
younger populations requires further investigation.
Acknowledgments
The authors would like to acknowledge the 19 cochlear implant
participants, for their dedication and willingness during the experimental
sessions.
Conclusions
In conclusion, the results presented in this study demonstrate
that cortical responses to an oddball paradigm, utilizing complex
stimuli, can be recorded with a single channel EEG acquisition setup from a CI population. This cortical evoked potential based
method can provide an estimated spectral ripple discrimination
threshold in adult CI listeners. Further research is required to
investigate the relationship of the objective assessment of spectral
resolution with speech perception scores, as well as to investigate
Author Contributions
Conceived and designed the experiments: ALV RBR MML FGZ LV JS
PW. Performed the experiments: ALV MML. Analyzed the data: ALV
MML RBR FGZ JS LV PW. Contributed reagents/materials/analysis
tools: LV JS PW. Wrote the paper: ALV MML RBR FGZ JS LV PW.
Critically revising the manuscript for important intellectual content; RBR
FGZ LV PW JS ALV. Subject recruiting for research; LV JS PW ALV.
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