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ScIEnTIFIc RePoRtS | (2018) 8:3825 | DOI:10.1038/s41598-018-22051-z
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New thin-lm surface electrode
array enables brain mapping with
high spatial acuity in rodents
W. S. Konerding1, U. P. Froriep2, A. Kral
1 & P. Baumho1
In neuroscience, single-shank penetrating multi-electrode arrays are standard for sequentially sampling
several cortical sites with high spatial and temporal resolution, with the disadvantage of neuronal
damage. Non-penetrating surface grids used in electrocorticography (ECoG) permit simultaneous
recording of multiple cortical sites, with limited spatial resolution, due to distance to neuronal tissue,
large contact size and high impedances. Here we compared new thin-lm parylene C ECoG grids,
covering the guinea pig primary auditory cortex, with simultaneous recordings from penetrating
electrode array (PEAs), inserted through openings in the grid material. ECoG grid local eld potentials
(LFP) showed higher response thresholds and amplitudes compared to PEAs. They enabled, however,
fast and reliable tonotopic mapping of the auditory cortex (place-frequency slope: 0.7 mm/octave), with
tuning widths similar to PEAs. The ECoG signal correlated best with supragranular layers, exponentially
decreasing with cortical depth. The grids also enabled recording of multi-unit activity (MUA), yielding
several advantages over LFP recordings, including sharper frequency tunings. ECoG rst spike latency
showed highest similarity to supercial PEA contacts and MUA traces maximally correlated with PEA
recordings from the granular layer. These results conrm high quality of the ECoG grid recordings and
the possibility to collect LFP and MUA simultaneously.
An important goal in neuroscience is to understand the activation patterns of neuronal networks at a high spatial
and temporal resolution. is knowledge is important in a variety of medical applications, such as hearing res-
toration via cochlear implants. Both following auditory deprivation and restoration of hearing, spatio-temporal
activation patterns of the auditory pathway are substantially altered1. e characterization of the spatio-temporal
activation patterns in the auditory cortex has commonly been assessed via recordings with penetrating
multi-electrode arrays (MEAs), which have the disadvantage of damaging the brain tissue2. is is especially
disadvantageous for chronic recordings in behaving animals2,3. Dierent MEAs can be used that cover a wide
range of temporal and spatial resolutions (~10 µm to 10 cm; for review see Lebedev & Nicolelis, 2017)4. One ver-
sion of MEAs is the laminar single-shank microelectrode array used for comparison in this study, which allows
conclusions on single cell activity5. In order to track ongoing changes in a chronic setting, for example aer dep-
rivation and following sensory restoration, spatially and temporally precise but non-invasive recording methods
are favorable. Both local eld potentials (LFPs) and action potential related activity are important to understand
the complex neuronal responses to sensory stimulation6. LFPs are commonly assumed to reect the input to the
dendritic eld, generated by synchronized synaptic activity, however, several electrical discharges may add to
these slow potentials (<100 Hz) including Na+ and Ca2+ spikes, ionic uxes through voltage- and ligand-gated
channels, and intrinsic membrane oscillations6,7. e outputs of the respective neuronal tissue are action poten-
tials, recorded extracellularly either as single spikes or as multi-unit activity (MUA), if they are recorded in some
distance from the active cells. If the number of underlying cells cannot be resolved due to small amplitude and/ or
high overlap, this spiking activity (>300 Hz) is sometimes referred to as “hash”6. LFP and spiking activity are not
mutually exclusive measures: First, LFPs indicate events that are causal to action potentials8 and secondly, MUA
activity is highly correlated with high-gamma (80–200 Hz) power of the brain oscillations9. It has been shown
that spatial characteristics of cortical responses, such as tonotopy, are represented at a ner spatial resolution by
spiking activity than by slow wave local eld potentials (LFPs) recorded with the same electrodes. is is due to
1Institute of AudioNeuroTechnology and Department of Experimental Otology, ENT Clinics, Stadtfelddamm 34,
Hannover Medical School, 30625, Hannover, Germany. 2Translational Biomedical Engineering, Fraunhofer Institute for
Toxicology and Experimental Medicine (ITEM), Nikolai-Fuchs-Strasse 1, 30625, Hannover, Germany. Correspondence
and requests for materials should be addressed to W.S.K. (email: konerding.wiebke@mh-hannover.de)
Received: 13 November 2017
Accepted: 16 February 2018
Published: xx xx xxxx
OPEN
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ScIEnTIFIc RePoRtS | (2018) 8:3825 | DOI:10.1038/s41598-018-22051-z
the fact that the eect of volume conduction is higher for LFPs and the small-amplitude, high frequency spiking
activity diminishes faster with distance8,10–12 (but see Herreras, 2016)13. However, spiking activity is commonly
only assessable using penetrating electrode arrays (PEAs). Subdural surface grids used in electrocorticography
(ECoG) permit simultaneous recording of multiple sites over a large area of the cortex without the need for
repeated (i.e. time consuming) penetrations of the brain tissue. But the combined eects of large distance to the
neuronal tissue, large electrode contact size and high impedances (>1 MΩ at 1 kHz) usually limit surface record-
ings to local eld potentials (LFP) and reduce spatial selectivity (usually >1 mm)4,14.
MUA recorded from the surface of the neocortex is mainly associated with activity of layer 1 interneurons as
well as pyramidal cells and interneurons in deeper layers15. In the study of Khodagoly and colleagues15, isolation
of single neuron action potentials was possible due to the neuron size, electrode diameter (10 µm) and the use of
an organic interface material that conducts not only electric, but also ionic current. However, gold or platinum
contacts are used in conventional ECoG recordings for both humans and animals14,16,17. Downscaled µECoG
electrode grids for human use, with 1-2 mm electrode diameter and -spacing, enable LFP recording which are of a
comparable spatial resolution to those of PEAs (i.e. several hundred µm)14. For animal studies, surface grids with
lower impedances (~200 kOhm) and smaller electrode diameters (150 µm) have been developed18,19. Additionally,
the introduction of thin-lm technology enables intimate electrode-tissue coupling for precise and reliable map-
ping of spatial activation and thus is also well suited for chronic recordings15,18,19. ese have already been shown
to be suitable to detect MUA, in recordings from the basal root ganglia20. Based on positive ndings in cats, but
not humans, the authors conclude that the intimate electrode-tissue coupling is a key factor for surface MUA
recordings and that low impedances and a exible substrate material are critical for high recording quality.
In the present study a thin-film surface ECoG grid was developed in cooperation with Blackrock
Microsystems, Europe, based on the current knowledge in the eld. e grids were then evaluated for their
potential to record spontaneous and evoked cortical activity. As there is currently no standard ECoG electrode
established for animal research, the data was compared to a conventional single-shank PEA (‘Michigan probe’)17.
We found that the new ECoG grids were suitable for recording both LFPs at high spatial resolution and MUA
comparable to simultaneous recordings from PEAs.
Materials and Methods
Animals and surgical preparation. e experiments were performed in 14 (2 female) Dunkin-Hartley
(albino) guinea pigs (372 ± 48 g). All procedures were in accordance with the German and European Union
guidelines for animal welfare (ETS 123, Directive 2010/63/EU) and were approved by the German state authority
(Lower Saxony state oce for consumer protection and food safety, LAVES; approval No. 14/1514). Normal hear-
ing was conrmed by auditory brainstem responses (ABRs; see acoustic stimulation for details).
Anesthesia was induced by an intra muscular injection of a combination of ketamine (50 mg/kg BW), xylazine
(10 mg/kg) and atropine sulfate (0.1 mg/kg). For subsequent inhalation anesthesia, a custom-made endotracheal
tube was inserted through a tracheotomy and connected to a ventilator (Rodent Ventilator 7025, Ugo Basile,
Comerio, Italy). Aer surgical preparation, an adequate anesthesia level for cortical recordings was maintained
by <1.5% isourane in O2/air and was surveyed by testing for paw-withdrawal and corneal reexes. Vital func-
tions were assessed by electro-cardiography (ECG) and capnometry (end-tidal CO2 vol%; Normocap CO2 & O2
Monitor, Datex, Helsinki, Finland). Body core temperature was kept at ~38.0 °C using a heating pad, controlled
via feedback from a rectal probe (TC-1000 Temperature Controller, CWE Inc., Ardmore, USA).
For xation in a stereotaxic frame (Stereotaxic Frame 1430, David Kopf Instruments, Tujunga, USA), the
skull was exposed and a head-holder (custom, stainless steel xation-rod) was secured to the bone anterior to
suture-point Bregma using 3 bone screws (Ø 0.85 mm, Fine Science Tools GmbH, Heidelberg, Germany) covered
with dental acrylic cement (Paladur, Heraeus Kulzer GmbH, Dormagen, Germany). For recordings from the
auditory cortex, a unilateral craniotomy (~5 × 5 mm) was performed, centered at ~2.5 mm caudally from Bregma
and 7.3 mm laterally from the midline. Aer removing the dura mater and positioning of the recording electrodes
the brain was covered with medical grade silicone oil (M 5000, Carl Roth GmbH & Co. KG, Karlsruhe, Germany)
to prevent dehydration through evaporation.
Recording electrodes. e size of the ECoG grid (4 × 4 contacts, 4 mm2 surface area; 0.5 mm contact
spacing) was chosen to cover the average area of the primary auditory cortex (A1) of the guinea pig. e sub-
strate for the grid was a 20 µm thin parylene C lm. e parylene C lm was metallized with gold, which was
photoresist-coated and developed, following direct writing (DWL 66 + , Heidelberg Instruments). Aer etching
of gold and removal of photoresist residuals the grid was connected to a standard Omnetics connector to enable
connection to the recording system. e nal grids had 16 AU contacts (100 µm in diameter) with impedances of
approximately 200 kΩ (see results). Dened openings in the parylene C substrate enabled simultaneous insertion
of a PEA (A1x16-5mm-150-177-A16, NeuroNexus, Ann Arbor, USA). For the characterization of responses to
broad-band stimuli, we recorded simultaneously from 14 cortical positions (12 animals) with 244 recording sites
for the ECoG grid and the PEA, respectively. To assess spatial selectivity in terms of tonotopy, we recorded from
21 surface positions overall (N = 14 animals, N = 336 ECoG recording sites) in combination with 18 positions
from a PEA with 16 channels and impedances of 1.0 ± 0.1 MΩ at 1 kHz (N = 288 recording sites). A silver ball
electrode covered in salt-free electrode gel (Spectra 360, Parker Laboratories INC., New Jersey, USA) was placed
through a hole ~1 mm rostral from Bregma onto the dura mater as a recording reference for both the ECoG grid
and the PEA It was sealed against leakage of cerebro spinal uid with surgical tissue adhesive (Histoacryl, B.
Braun Melsungen AG, Melsungen, Germany) and covered with dental acrylic cement.
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Acoustic stimulation. Prior to surgery, ABR click thresholds (i.e. lowest sound intensity inducing a visual
discernable response) were assessed via two trans-dermal silver wire recording electrodes: one at the vertex and
one as retro-auricular reference electrode. e 50 µs condensation clicks were presented via an audiometric head-
phone speaker (DT48, Beyerdynamic, Heilbronn, Germany) in een 5-dB steps (sound pressure level indicated
as peak equivalent: 26–91 dBSPL pe), with 50 repetitions each. Normal hearing was assumed for ABR thresholds of
≤35 dBSPL pe.
All other acoustic stimuli were presented in randomized order with 30 repetitions each (recording interval:
827 ms). Stimuli were delivered via the audiometric headphone speaker to the ear contralateral to the cortical
recording side. e stimulation was performed in closed eld condition, using a custom build polyvinyl chlo-
ride cone, xed to the speaker and set onto the external meatus. e responsiveness to broad-band stimuli was
accessed using 100 ms white noise bursts (10 ms cosine ramps), presented in seventeen 5-dB steps (sound pressure
level indicated as root mean square value: 0–80 dBSPL rms). To describe the tonotopic organization of A1, pure
tones (100 ms, 10 ms cosine-ramps) at frequencies between 1 kHz and 32 kHz with usually 0.5 octave increments
were presented in nine 10-dB steps (0–80 dBSPL). e sound levels were calibrated prior to the experiments at
the tip of the stimulation cone through the stimulation soware (AudiologyLab, Otoconsult, Frankfurt a.M.,
Gemany) using a condenser microphone (1/4″ microphone [4939] in combination with a preamplier [2670] and
a Nexus conditioning amplier [2690], Brüel & Kjaer, Nærum, Denmark).
Data recording and analysis. Recordings of both the ECoG grid and the PEA were performed using a
custom build recording setup and soware (AudiologyLab, Otoconsult, Frankfurt, Germany). e signals were
acquired and amplied through a multichannel recording system (Lynx-8 amplier system, amplication 8000
or 5000 times, butterworth lter: 1 Hz–9 kHz, rollo: 12 dB per octave, Neuralynx, Bozeman, USA) and stored
through AudiologyLab at a sampling rate of 25 kHz using a 32-channel MIO card (NI-6259 National Instruments,
Austin, USA). e three deepest PEA contacts were excluded from the analysis, as they were commonly inserted
beyond the grey matter of the AC due to the length of the PEA. These contacts usually did either show no
responses or did not record activity above the threshold criteria described below.
e peak to peak amplitude of the LFP (resampled at 2 kHz via a Matlab routine) was analyzed from 0–200 ms
aer stimulus onset. MUA spike rates (spikes/stimulus/ms) were measured from 10–40 ms aer stimulus onset.
MUA was calculated as described previously21. In short, the signal was ltered (zero-lack, 2nd order elliptic lter:
300–3000 Hz) and spikes with at least 0.08 ms duration above the detection threshold (i.e. 3* standard deviation,
SD, based on median activity of the signal)22 were counted as MUA. e level of background activity was meas-
ured during a 50 ms pre-stimulus time window and was subtracted from the respective measure (LFP amplitude
or MUA rate) during the post-stimulus time. For comparison of onset-response timing between ECoG grid and
PEA, the rst-spike latency (FSL) was calculated from stimulus onset, as the time when the given spike train dif-
fered signicantly (p < 0.001) from a Poisson distribution23.
As measure of overall responsiveness, we described input-output functions for broad-band noise stimuli.
We excluded data sets as ‘non-responsive’ for which the highest sound intensity did not evoke at least twice the
average background activity, calculated as mean value over all intensities for each contact. We tted the functions
with a sigmoidal t (e.g. Konerding et al., 2017)24 and derived the response threshold (i.e. sound intensity [dB]
inducing 10% of maximal response amplitude) and dynamic range (90% of max–10% of max). In individual
cases, the highest sound intensity (80 dBSPL rms) induced lower response amplitude than the second highest (75
dBSPL rms), potentially due to the middle ear muscle reexes25. In these cases, the tting was performed without
including the responses to 80 dBSPL rms. To gain a comparable measure of response strength for MUA and LFP,
we calculated the response-background ratio (RBR) at 20 dB above response threshold as dB above the average
background activity. As the MUA is mainly a binary measure, we assessed the RBR in terms of changes in spike
rate above background MUA rate26.
e tonotopic organization of the auditory cortex was determined based on the characteristic frequency (CF,
frequency that elicits responses at lowest sound intensities) for each recording site. To calculate the CF, we inter-
polated a tuning curve at 10% of the maximal LFP amplitude or MUA rate of a given contact27, if the maximum
exceeded 3 times the standard deviation of the background activity. e tuning width was calculated in terms of
Q20-values: CF divided by the bandwidth of the tuning curve 20 dB above the sound intensity at CF (from here:
CF threshold). For visual inspection of the tonotopy, we dened CFs with a resolution of 1 octave and plotted all
contacts, spatially aligned based on arterial and suture landmarks. Based on the border of A1 to the dorsal cortex
(DC), as inferred from a frequency reversal, we dened the tonotopic axis orthogonal to the border. To compen-
sate for inter-individual dierences in cortex structure, we normalized the data according to the coordinates of
the 8 kHz CF (taking the mean if there were several sites with 8 kHz for one individual). Subsequently, a linear
regression was calculated along this tonotopic axis to assess the spatial tonotopy resolution in mm/octave, for
each individual and for the whole data set.
e subdural ECoG grid was compared to the PEA with regard to both horizontal and laminar distance.
For the CF distribution along the surface, the PEA contact with lowest CF threshold is compared to the ECoG
grid contacts pooled according to distance from the insertion point: close = 0.5 mm (i.e. 4 inner contacts),
medium = 1 mm, far = 1.3 mm (Fig.1A). For every distance, the CF dierence between ECoG grid and PEA
contacts is calculated in octaves. If several PEA contacts showed the same low CF threshold, the median CF was
chosen (two data sets excluded, as no CF was discernable at the PEA). To estimate the laminar contribution to
the ECoG recordings, we assessed the similarity in FSL and the correlation strength. e FSL was calculated
for the best frequency (BF). e BF was dened as the frequency that elicited highest summed spike rates over
all sound levels. It was used as non-interpolated estimate of the CF (based on MUA), since both measures were
highly correlated (spearman correlation: p < 0.001, r = 0.915 N = 317; Supplement Fig.1). We used the mean FSL
of the four ECoG contacts surrounding the insertion point and pooled the PEA contacts based on the estimated
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location in dierent cortical layers: supragranular layers: 0–600 µm (contact #1–5), granular layer: 800–1100 µm
(contact #6–9), infragranular layers: 1350–1800 µm (contact #10–13). e correlation between the ECoG grid
recordings and the PEA was assessed in response to noise bursts to exclude the inuence of frequency selectivity.
e correlation strength was assessed as two-dimensional (time and repetition) Pearson correlation (r2) between
the 4 ECoG contacts closest to the insertion point and all 16 PEA contacts, using single sweeps (autocorrelation:
r2 = 1). Recordings were excluded whenever clipping was observed in more than 2 individual sweeps. To assess
the correlation of LFPs, we analyzed the raw signals in a time window of 0–200 ms from stimulus onset. To corre-
late spiking activity at the surface and in the depth we transferred the MUA event train into a continuous signal by
calculating the moving average (1 ms integration width). As an estimate of overall correlation between the ECoG
grid and the PEA, we calculated a sweep-wise sum over all 16 PEA contacts. e surface contact with the highest
correlation to this summed signal was used to analyze the eect of insertion depth, correlating each of the 16
channels, separately, with the respective ECoG contact.
Figure 1. Recordings from the newly developed ECoG grid were possible for all 16 surface contacts and
were similar to simultaneous recordings from a conventional penetrating multi-electrode array. (A) Sketch of
the subdural ECoG grid. Markers on the grid facilitate the allocation to the respective surface locations. e
distances to the central insertion point are indicated (a = 0.5 mm, b = 1.0 mm, c = 1.3 mm) (B) e penetrating
multi-electrode array (PEA) is inserted through an opening in the ECoG grid substrate. Marked are the pseudo
sylvian sulcus (PSS) and the middle cerebral artery (MCA), which served as landmarks for spatial alignment
of dierent recording positions. (C) Example image of a 16 channel single shank PEA. (D) Examples of traces
(raw signal) for an ECoG grid and a penetrating multi-electrode array from the same cortical recording during
spontaneous activity.
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Statistical analysis. Considering the focus of this study and due to the difference in electrode con-
tact characteristics (e.g. size and impedance), we assessed the recordings of the ECoG grid and PEA contacts
as independent measures and performed unpaired analyzes for the comparison of LFP characteristics at both
electrodes. We performed paired comparisons for LFP and MUA recorded at the same electrode contact. e
Kolmogorov-Smirnov test was used to conrm normal distribution of the data and, if required, non-parametric
analyzes were applied and the median ± interquartile range (IQR) instead of the mean ± standard deviation (SD)
was calculated. e level of signicance was set to 5%.
Results
We rst described the properties of the new ECoG grid and its potential for recording spontaneous activity
from the auditory cortex. Subsequently, we showed the similarity of LFP recordings from the ECoG grid to
simultaneously recorded LFP of PEAs. We assessed both the evoked responses to broad-band stimuli in terms of
input-output functions and showed stimulus specicity in terms of tonotopy, using tonal stimulation. To explore
the origin of the ECoG recordings, we furthermore correlated subdural recordings with recordings from dierent
cortical depths. Finally, we described the potential of the ECoG grid to record MUA from the surface and related
the derived measures to those of the LFP recordings.
ECoG grid design and spontaneous activity recording. e grid substrate parylene C achieved high
exibility and enabled an intimate contact at the electrode-tissue interface. e average impedance was 212 ± 51
kΩ (measured at 1 kHz; n = 5 grids), ranging from 128 to 395 kΩ (one extreme value with 962 kΩ). Individual
ECoG grids were used up to six times, without observable changes in recording quality. Simultaneous recordings
from a PEA inserted through one of the openings in the grid material were possible with high signal quality
(Fig.1).
Evoked LFP responses to broad-band stimuli. To assess the potential of the new ECoG grid to record
brain activity during sensory stimulation (Fig.2A), we analyzed the evoked LFP responses to broad-band stimuli
recorded with the ECoG grid and compared these to simultaneous recordings from PEAs at 14 cortical positions.
Of 224 recording sites, 126 on the ECoG grid showed a clear onset response (56.3%; criterion: >2* background
activity) while this was the case for 58 (31.9%) recording sites on the PEA (N = 182; 3 deepest contacts excluded).
e input-output functions (Fig.2B) showed some variability across recording sites (Fig.2B), and the sigmoi-
dal t was not possible for 15 ECoG and 4 PEA responsive sites. In the remaining data sets, the goodness of
t was high (ECoG: r2 = 0.956, SD: 0.046, n = 111; PEA: r2 = 0.955, SD: 0.025, n = 54). e calculated maximal
peak to peak amplitude was signicantly higher for the ECoG (median: 1.177 mV) as compared to the PEA
(0.478 mV; Mann-Whitney U test: p < 0.0001, U = 1475; Fig.2C). e response-background ratio at 20 dB above
response threshold was higher for the ECoG grid (median: 0.455, n = 101, negative values excluded) than for the
PEA (median: 0.292, n = 52, negative values excluded; Mann-Whitney U test: p < 0.0001, U = 1308; Fig.2D).
e calculated response threshold was higher at ECoG contacts (median: 32.46 dBSPL rms) than at PEA contacts
(21.75 dBSPL rms; Mann-Whitney U-test: p < 0.0001, U = 1817; Fig.2E). e dynamic range was however similar
for ECoG grid and PEA contacts (medianECoG: 29.79 dB; medianPEA: 36.10 dB; Mann-Whitney U test: p = 0.369,
U = 2738).
Tonotopy based on LFP recordings. To determine the spatial selectivity of recordings from the ECoG
grid, we assessed the frequency selectivity of each recording site and the change in characteristic frequency
(CF) with cortical position (i.e. tonotopy). For each recording site at the ECoG grid (N = 336) and at the PEA
(N = 234), we determined the CF and the tuning width in terms of Q20-values from the frequency response
curves based on LFP recordings (Fig.3A). Based on the dened CF threshold (criterion see methods), 12% of
the ECoG recording sites were dened unresponsive and were excluded from the analysis. In another 10% no
single CF could be determined, due to the double-peaked appearance of the frequency tuning curve (FTC).
Additionally, there was 1 case in which the frequency response area showed no clear FTC and in 15 cases the CF
was at or possibly below 1 kHz (lowest stimulated frequency) and no Q20-value could be assessed. Of the PEA
recordings, 41% were dened unresponsive (i.e. fell below CF threshold criterion), 13% of the FTCs were double
peaked, 3% had a CF at 1 kHz and 1 case (not the same animal as for the ECoG grid) had a complex response
characteristic.
e average Q20 tuning width was similar for the surface and penetrating electrodes (medianECoG = 0.995,
medianPEA = 0.916; Mann-Whitney U test: p = 0.361, U = 11411, nECoG = 246, nPEA = 99). is dierence was not
based on a dierences in CF, as ECoG and PEA contacts had similar CF values (medianECOG = 8.1, medianPEA =
8.0; p = 0.797, U = 26381, nECoG = 261, nPEA = 205). e spatial distribution of CF of the combined ECoG record-
ing sites showed a frequency reversal, which marks the transition from the primary auditory cortex (A1) to the
dorsocaudal cortex (DC, Fig.3B).
Based on a virtual line of the medio-lateral frequency reversal we dened the orthogonal, rostro-caudal tono-
topic axis. All CFs within A1 were correlated with their cortical coordinates relative to the tonotopic axis and for
each individual we derived the slope of the resulting linear regression (Fig.3C). One animal was excluded due to
very poor tting (r2 = 0.057), the remaining 13 datasets had a high coecient of determination (r2 = 0.709, SD:
0.139). e slope for all data was 0.678 mm/octave (r2 = 0.685). e individual slopes had a low spread around
the mean (0.646 mm/octave) with a standard deviation of 0.153 mm/octave and a range from 0.373 to 1.000 mm/
octave.
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Correspondingly, the CF dierence between the ECoG and PEA contacts was around 1 octave for contacts
close (0.5 mm) to the insertion point (mean ± SD: 0.918 ± 0.869 octave) and signicantly increased with horizon-
tal distance (repeated measure 1-way ANOVA: p < 0.0001, n = 16; Fig.3D).
Correlation with laminar distance. In order to determine the origin of the LFP signals recorded at the ECoG
grid, we correlated the signal with those recorded via the PEA. e correlation strength (r2) signicantly declined
with cortical depth from channel #1 to #16, with a high correlation for the most supercial channel #1 (r2 = 0.411)
and weakest correlation for channel #16 (r2 = 0.036; 1-way repeated measure ANOVA: p < 0.0001, F = 8.426,
df = 15; Fig.4). e comparison with the correlation strength between ECoG grid and summed signal of the PEA
(r2 = 0.190) revealed that only the signal recorded at the rst contact showed a signicant higher correlation with
the ECoG grid recordings. e summed signal of the PEA explained less variance of the ECoG recordings than
each of the 4 supercial PEA contacts, individually.
Figure 2. e new ECoG grid enabled recordings of evoked LFP responses similar to conventional penetrating
multi-electrode arrays. (A) Examples of evoked responses to a 100 ms broad-band stimulus for an ECoG grid
and a penetrating multi-electrode array (PEA) from the same cortical recording. Given are 30 single sweeps
(thin grey lines) and an averaged signal (thick black line). Stimulus onset is indicated (red arrow head). (B)
Input-output functions of LFP peak-to-peak (p2p) amplitudes in response to a broad-band stimulus. e colors
represent dierent recording positions; the three deepest PEA contacts (excluded from further analysis) are
indicated in light grey. e amplitudes at the penetrating electrode (PEA) are signicantly smaller than those
at the ECoG grid (C and D). (E) e response thresholds at ECoG grid contacts was slightly higher (i.e. worse)
than those recorded at the penetrating electrode (PEA) contacts. (C) e maximal peak to peak (p2p) amplitude
at ECoG grid contacts was signicantly higher than those recorded at the penetrating electrode (PEA) contacts.
(D) e response-background ratio (RBR) at 20 dB above response threshold was signicantly higher at ECoG
than at PEA contacts. (C–E) Given are individual data (dots) and medians with IQR (lines). Mann-Whitney U
test: ***p < 0.0001.
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Comparison of MUA activity at the ECoG grid to the PEA. We analyzed the high frequency component (300–
3000 Hz) of the recorded signal that appeared as hash in the single traces (Fig.5). From 224 and 182 record-
ing sites at the ECoG grid and the PEA, respectively, 193 (86.2%) and 83 (45.6%) were dened responsive to
broad-band stimuli based on a MUA rate (Fig.5) of at least twice the background activity. e MUA had a clear
onset response with a sigmoidal input-output function (Fig.5B). Usually, also a weak oset response was discern-
able (Fig.5C, data not analyzed).
To determine the potential origin of MUA recorded at the ECoG grid, we compared the FSL between ECoG
and PEA contacts and also derived the correlation strength between the respective signals. The mean FSL
(22.65 ms) of the four surface contacts surrounding the insertion point was similar to those recorded by super-
cial contacts of the PEA (depth 1/ supragranular layer: 24.33 ms, depth 2/ granular layer: 26.27 ms) and diered
signicantly from the one in deeper, infragranular layers of the cortex (depth 3: 27.45 ms; dependent t-tests with
Bonferroni correction: surface vs depth1 p = 0.792; surface vs depth2 p = 0.342; surface vs depth3 p = 0.012,
N = 15, n = 3 comparisons; Fig.5C).
e correlation strength of the MUA between the ECoG and the PEA contacts showed signicant changes
with cortical depth (ANOVA: p < 0.001, F = 4.422, df = 15; Fig.5D). e summed signal over all PEA contacts
yielded a signicantly higher correlation strength with the ECoG grid (r2 = 0.191) than most of the individual
Figure 3. e ECoG grid enabled ne-scale tonotopic mapping of the GP auditory cortex based on LFP
recordings. (A) Example of frequency response curves derived by ECoG grid recordings and recordings from
penetrating electrodes. Indicated are the CF and the lower and upper value 20 dB above CF, from which Q20-
values were calculated. (B) Tonotopic map of the auditory cortex. For each ECoG grid contact (NLFP = 241) the
CF in octave increments was plotted over the respective cortical coordinate. A frequency reversal (dashed line)
marks the transition from the primary auditory (A1) to the dorsocaudal cortex (DC). (C) Correlation of CF
with the tonotopic axis of A1 for LFP recordings from all ECoG grid recording sites. Given are individual data
(dots) and regression lines per individual (gray, thin lines), as well as the overall regression (thick solid line)
with the 95% condence interval (dashed lines). (D) e CF distance in octaves between ECoG grid recording
sites and penetrating multi-electrode array (PEA) increased with increasing surface distance. Given are
individual data (dots) and mean with SD (lines). Repeated measure 1-way ANOVA p < 0.002 with Bonferroni
post-test: ***p < 0.001.
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contacts. Only the correlation strength between the ECoG grid and the 7th contact (~900 µm deep, r2 = 0.142) was
not signicantly dierent from the one with the summed signal and also had signicant higher values than most
of the electrode contacts inserted deeper into the cortex. us, the summed signal of the PEA did not explain
signicantly more variance in the surface recordings than the penetrating contact #7 on its own.
Comparison between MUA and LFP measures at the same ECoG grid and PEA contacts. To assess whether the
MUA assessment gave information in addition to the LFP measures, we rst compared the activity to broad-band
stimuli with regard to the response threshold, dynamic range and the response-background ratio (based on LFP
amplitude or MUA rate) at 20 dB above response threshold. e sigmoidal t of the MUA input-output func-
tions was not possible for all contacts. e respective sample sizes for paired data (i.e. LFP and MUA at the
same contact) are given below. e results were not specic to the ECoG grid, but were also found for the PEA.
e response-background ratio was signicantly higher for MUA (ECoG: 1.589, PEA: 1.122) compared to LFP
measures (ECoG: 0.451, PEA: 0.209; Wilcoxon test: ECoG: p < 0.0001, W = −4465, n = 94; PEA: p < 0.0001,
W = −276, n = 23; Fig.6A). e response threshold was signicantly higher for MUA (ECoG: 35.87 dBSPL rms,
PEA: 30.49 dBSPL rms) compared to LFP measures (ECoG: 32.40 dBSPL rms, PEA: 23.29 dBSPL rms; Wilcoxon test:
ECoG: p < 0.0001, W = −37490, n = 94; PEA: p = 0.0002, W = −242, n = 23; Fig .6B). e dynamic range was
signicantly lower for MUA (ECoG: 21.58 dB, PEA: 26.62 dB) compared to the LFP (ECoG: 30.27 dB, PEA:
39.18 dB; Wilcoxon test: ECoG: p < 0.0001, W = 3049, n = 102; PEA: p = 0.029, W = 154, n = 24; Fig.6C).
To assess whether the spatial selectivity of the ECoG grid MUA recordings was higher than the one derived
from LFP recordings, we compared the frequency selectivity measures CF slope along the tonotopic axis and the
Q20-values. e slope for all MUA data was 0.712 mm/octave and individual slopes (min: 0.419, max: 1.890 mm/
octave) did not dier signicantly from those derived by LFP measures (Wilcoxon signed rank test: p = 0.588,
W = −17, n = 13 animals; Fig.6D). As for the LFP recordings, the CF dierence between the ECoG and PEA
was below 1 octave for contacts close (0.5 mm) to the insertion point (mean ± SD: 0.937 ± 0.710 octave) and
signicantly increased with increasing distance (repeated measure 1-way ANOVA: p = 0.010, n = 16). e MUA
Q20 tuning width was similar for ECoG and penetrating electrodes (medianECoG = 1.043, medianPEA = 1.073;
p = 0.343, U = 10255, nECoG = 239, nPEA = 92). However, when comparing the LFP tuning widths to those of the
MUA, the MUA Q20-values (ECoG: 1.053, PEA: 1.075) were signicantly larger than the LFP Q20-values (ECoG:
0.979, PEA: 0.823), indicating a slightly higher spatial resolution derived by MUA. is was the case both for
the PEA (Wilcoxon signed rank test: p = 0.009, W = −663, n = 55) and for the ECoG (p = 0.040, W = −2918,
n = 206; Fig.6E). is dierence in tuning width was not based on a dierence in CF (with higher CF having
higher Q-values), as the corresponding CF-values were similar or even lower for MUA than for LFP measures
(ECoG: medianLFP: 8.1, medianMUA : 8.1, p = 0.816, W = −229, n = 220; PEA: medianLFP: 6.8, medianMUA: 2.4,
p < 0.001, W = 1366, n = 71).
Discussion
Subdural ECoG grid recordings allow scanning large brain areas by using multiple recording sites, without dam-
aging the underlying brain tissue. e newly developed ECoG grid (Blackrock Microsystems Europe) was found
to yield similar recording quality as a conventional PEA (i.e. Neuronexus ‘Michigan probe’). e impedances of
the ECoG grids were low (mean 212 kΩ) and showed little variability between contacts (SD: 51 kΩ; one extreme
at 962 kΩ). e thin-lm material allowed intimate contact to the brain surface and recordings of spontaneous
brain activity could be obtained from all ECoG grid contacts. Recording quality was not compromised by cover-
ing the grid and brain by silicone oil. Evoked responses could be reliably assessed in a vast majority of the record-
ing sites and the incidence of non-responsive sites was much lower than for the PEA for both broad-band stimuli
Figure 4. Correlation strength (r2) between grid and penetrating electrode recordings for LFP. Given are mean
(dot) and SEM (whisker) and the non-linear regression line (One-phase exponential decay: r2 = 0.370). Base d
on the raw signal, we revealed that recordings of the grid and the penetrating (PEA) contacts signicantly
declined with cortical depth (contacts #14–16, indicated in light grey, were excluded from further analyses).
Beyond 5 contacts (~ 750 µm), r2 fell below the summed sweep-by-sweep correlation strength over all 16
penetrating contacts (red). ANOVA with Bonferroni corrected posttest: **p < 0.01.
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(26 vs. 54%) and pure tones (13 vs. 47%). Although we did not systematically assess the stability of the ECoG grid
over several recording sessions, we observed persisting high recording quality for up to six experiments, using
the same ECoG grid. We found that these new ECoG grids were suitable for recording both LFPs at high spatial
resolution and MUA comparable to conventional PEAs.
By recording simultaneously from the surface ECoG grid and the PEA, we could directly compare evoked
LFP responses between the two electrode types. The ECoG grid LFPs were on average larger, had a higher
response-background ratio and showed slightly elevated response thresholds as compared to the PEA recordings.
e dynamic range was similar between the electrode types.
Figure 5. e newly developed ECoG grid enabled recordings of evoked MUA similar to conventional
penetrating multi-electrode arrays. (A) Examples of ltered (300–3000 Hz) traces with detected spikes (red
circles) above detection threshold (dashed line) for both ECoG and penetrating multi-electrode array (PEA).
Stimulus onset is indicated (red arrow head). (B) Representative examples of averaged LFP and raster-plots
of MUA in response to a broad-band noise stimulus of 80 dBSPL rms. e raster-plots indicate the time points
of every detected spike in each of the 30 repetitions. Stimulus onset is indicated (vertical line). (C) e peri-
stimulus time histograms (psth) show representative responses to a 100 ms broad-band stimulus, for the ECoG
grid and the penetrating electrode (PEA), respectively. Usually a strong onset response and a weak oset
response were discernable. (D) Similarity between rst spike latency (FSL) for ECoG grid and penetrating
multi-electrode array contacts decreases with cortical depth (depth3 = infragranular layer). Given are median
FSLs at the best frequency threshold level (i.e. 10% of max MUA, comparable to the CF threshold) for ECoG
grid recording sites close to the insertion point and for dierent depths at the PEA (depth 1: 0–600 µm, depth
2: 750–1200 µm, depth 3: 1350–1800 µm). Box-plots with min and max values (whisker) and mean (cross). e
FSL at the surface was signicantly shorter than the one recorded in deep layers of the AC; dependent t-tests
with Bonferroni correction *p < 0.05. (E) Correlation strength (r2) between grid and penetrating electrode
recordings for MUA is highest for granular layers (contact #7). Given are mean (dot) and SEM (whisker). Aer
an initial decline in correlation strength, the maximal correlation was reached at the 7th contact and sharply
declined with increasing cortical depth (contacts #14–16, indicated in light grey, were excluded from further
analyses). ANOVA with Bonferroni corrected posttest: *p < 0.05, **p < 0.01, ***p < 0.001.
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One potential explanation for signicant dierences between surface and penetrating electrode recordings
would be dierences in cortical depth. However, we did not nd any indications in our data (analyzes not shown)
and to the best of our knowledge, no such dierence have been reported in the literature28. e dierence in
response threshold was unexpected and the distribution of values highly overlapped between the two electrode
types. A dierent involvement of input sources, including long latency activity, may have led to a higher variance
in individual surface recordings and the observed small increase of ECoG grid response thresholds in the aver-
aged signal, as compared to the depth recordings. Larger amplitudes at the ECoG grid contacts as compared to the
PEA may arise due to lower impedances (200 Ωk vs 1 ΩM). However, the inuence of impedance values and elec-
trode contact geometry is discussed to be rather negligible for LFP recordings29. Additionally, larger amplitudes
and higher response-background ratios may arise from recordings from a higher number of synchronously active
neurons7. is would indicate that integration occurs over more neurons at the ECoG grid electrode as compared
to the penetrating electrode contacts. However, the high spatial acuity and similarity in tuning width (see below)
Figure 6. e characterization of the surface responses diered signicantly between MUA and LFP
measures at the same contacts. (A) e response-background ratio (RBR) 20 dB above response threshold was
signicantly higher (i.e. better) for MUA (based on rate) as compared to LFP (based on amplitude). Given are
individual data (dots) and medians with IQR (lines). Wilcoxon test: ***p < 0.0001. (B) e response threshold
was signicantly higher (i.e. worse) for MUA as compared to LFP measures. Given are individual data (dots)
and medians with IQR (lines). Wilcoxon test: ***p < 0.0001. (C) e dynamic range was signicantly lower
for MUA as compared to LFP measures. Given are individual data (dots) and medians with IQR (lines).
Wilcoxon test: *p < 0.05, ***p < 0.0001. (D) e MUA Q20-values were signicantly larger (i.e. sharper tuning)
compared to LFP Q20-values. Given are individual data (dots) and median with IQR (lines). Wilcoxon test:
*p < 0.05, ***p < 0.0001. (E) e tonotopy derived by MUA measures (black) at the ECoG grid was similar to
the one derived by LFP measures (light orange). Given are individual data (dots) and the linear regression with
the 95% condence interval (solid and dashed lines).
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showed that the ECoG grid contacts did not integrate over a signicantly larger amount of cells, at least in the
horizontal plain. Furthermore, the size of LFP amplitudes is discussed to be inuenced by the cytoarchitectonic
structure rather than by functional factors13. e fact that the surface electrode is recording from the outside of
the activated tissue, in a dierent orientation to the LFP source(s), as compared to the PEA, may therefore explain
the signicant dierences in LFP magnitude.
e ECoG grid enabled fast and reliable assessment of the tonotopic map, revealing the typical tonotopic
organization of the auditory cortex with a frequency reversal dening the transition between A1 and DC. Most
of the derived frequency tuning curves (FTC) were V-shaped and allowed the designation of a CF and calcu-
lation of Q20-values. Only in about 10% of both the surface and the depth recordings double-peaked FTC30
were found. is corresponds well to observations of uniform V-shapes under anesthesia with increasing num-
bers of multi-peaked FTC in awake animals, e.g. 20% of neurons in marmosets (for review see Schreiner et al.,
2011)31. e individual frequency slopes along the tonotopic axis were highly consistent and the average slope was
0.68 mm/octave (0.71 mm/octave for MUA). is value is similar to the one reported by Hellweg and colleagues
of 0.63 mm/oct ave32 and corresponds well to frequency distributions in A1 reported in the literature15,30,33. ese
were derived by multiple insertions of penetrating electrodes30,32,33 or by optical imaging methods15. Based on
the guinea pigs broad hearing range of 0.05–50 kHz (~9.5 octaves)34 and the size of A1 of 4 mm along the tono-
topic axis (Fig.3C and30), a slope of 0.42 mm/octave would be required to evenly represent the whole frequency
range. is theoretical slope is considerably steeper than the values derived by mapping the auditory cortex. e
discrepancy may be based on under-representation of parts of the hearing range, or omissions of specic frequen-
cies. e two eects have been described in several mammalian species and are both discussed to reect specic
environmental adaptations31. In our data, the distribution of frequencies shows an obvious gap at around 4 kHz,
which has also been shown by previous recordings from the guinea pig auditory cortex12. We assume that this gap
is based on an adaptive response (either plastic change or inborn characteristic) to the resonance frequency of the
bulla, which is at around 4 kHz35. is frequency would dominate the auditory percept, if it was not dampened
or ltered out. is process is most probably established from the cochlea onwards, as an underrepresentation of
CFs around 4 kHz is already apparent in auditory nerve recordings36. A comparison of the ECoG grid tonotopy
to one derived by PEAs was not possible in the present study, as we did only use one penetration per surface
recording position to prevent severe brain damage due to extensive sampling. Besides of the reported similarity to
the tonotopic organization in guinea pig A1 derived by dierent recording methods (see above), we furthermore
compare the Q20-values of the surface and penetrating electrode and found no signicant dierence in sharpness
of tuning.
A detailed analysis of the source and spread of the LFP recordings was not within the scope of the study. We
did however assess the correlation between ECoG and PEA recordings. e results showed that the signal at the
surface was most similar to the ones in supercial layers and the similarity exponentially decreased with cortical
depths. is, together with the high (spatial) frequency selectivity, indicates that the LFP recordings in our study
are quite local. Similar changes in the LFP signal over only few 100 µm have been reported in the literature29. e
actual spread may however be less specic for the type of recording electrode and be rather a characteristic of the
recorded brain structure and sensory modality13.
MUA at the ECoG contacts was similar to the one recorded at the PEA, with similar psth response pro-
les and input-output characteristics. When comparing MUA to LFP recordings, MUA yielded higher response
thresholds, lower dynamic ranges and higher response-background ratios (based on MUA rate)26. e tonotopic
slope was similar to the one derived from LFP measures. However, the Q20-values were slightly higher (sharper
tunings) for the MUA as compared to the LFP recordings. All these dierences were not specic for the surface
ECoG grid, but were also obvious at the PEA, conrming the general dierence between MUA and LFP record-
ings. Most of the results are in line with the notion that MUA is spatially more selective, integrating activity over a
smaller sample of neurons, than the LFP recordings8. e higher spatial selectivity will additionally be sharpened
by lower volume conduction for MUA as compared to LFP, with less overlap between sources12 and by dierences
in the characteristics of the sources13. As the LFP is dominated by the cortical input, whereas the MUA corre-
sponds to the output of the respective neurons, lower frequency selectivity in sub-cortical as compared to cortical
structures will aid to the observed dierence in frequency selectivity37. e elevated response thresholds for MUA
(see also Norena & Eggermont, 2002)37 can also be interpreted this way, considering that at response threshold the
synchrony of activation is important for detection and that variance in a small set of neurons (MUA) will lead to
higher uncertainty than the same variance in a larger set of neurons (LFP). Additionally, LFPs may be inuenced
by cortical input that is too small in amplitude to generate local MUA37. e higher response-background ratio at
20 dB above response threshold is likely to be based on dierences in background activity for LFP amplitudes as
compared to MUA rates. LFP recordings, integrating over a larger population of neurons, will result in a higher
background activity than MUA that is based on a smaller set of neurons. However, due to the dierence in the
stimulus characteristics (eld vs. spikes), a direct comparison between the levels of background activity was not
possible.
e fact that we revealed very similar slopes for CF-changes along the tonotopic axis for both LFP and MUA
measures seemed at rst sight to be contradictory to the assumption of a higher spatial acuity for MUA record-
ings. For example, Fallon and colleagues17 reported that they needed to calculate the second spatial derivative
(SSD) to reveal similar tonotopic slopes of surface LFP recordings compared to MUA PEA recordings. However,
for the visual cortex (V1) of macaques, Xing and colleagues38 found similar visual eld maps for simultaneously
recorded LFP and MUA at the same penetrating electrode and CF values have typically been reported to be
similar between LFP and MUA PEA recordings12,37. We assume that the contact size and distance of the (surface
or penetrating) recording electrodes to the respective neurons, in relation to their functional selectivity (e.g.
frequency tuning), is determining whether or not the higher spatial acuity of MUA recordings will result in sig-
nicant dierences of local characteristics (e.g. CF), as compared to LFP recordings.
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To assess the link between ECoG and PEA MUA we rst compared the FSL and revealed similar FSL between
the ECoG grid recordings and recordings from supragranular layers. Furthermore, the MUA trains at the surface
were best correlated with trains recorded at the granular layer IV (800–1100 µm)28. As the granular layer showed
higher spiking activity than supragranular layers (Fig.5A), this may be an artifact of the correlation computation,
as lower correlation coecients may result from less variability in the data (i.e. few spikes)39. However, adding the
signals of all 16 contacts did not result in further signicant improvement of the correlation. us, we conclude
that the MUA recorded at the ECoG grid corresponds to supragranular and granular (~900 µm depth) spiking
activity. It is however unlikely that an individual granular spike is picked up at a subdural ECoG contact, as the
horizontal spread of MUA is supposed to be around 200 µm6,15,40,41. We assume that the MUA (or hash) recorded
at the surface corresponds to a summation of simultaneous spiking activity40 of layer 2/3 pyramidal cells, which
may also include activity from layer 1 interneurons15 and backwards propagating, dendritic spikes from deeper
layers42. Further research, such as intracortical electrical stimulation43 and pharmacological blockage, will be
necessary to characterize the origin of subdural spiking activity recorded with the ECoG grid.
e fact that the newly developed surface electrode also enabled recordings of MUA was quite unexpected, as
previous publications usually do not document this feature (for review see Im & Seo, 2016)44, or link it to specic
properties of the electrode, such as organic interface materials15. However, gold and platinum ECoGs have already
been described to be able to record MUA from basal root ganglia in the cat, but not in humans, and the authors20
conclude that the distance between electrode and tissue might be the key issue for recording unit-activity and
suggest that reduced impedances and exible substrate material would improve the recording quality. e ECoG
grid in our study addressed both and provided a exible and adhesive substrate with low impedance contacts.
Due to the lack of systematic analysis of the potential to record surface MUA in current publications (probably
related to a reluctance to report negative results), we cannot estimate which material characteristics of the ECoG
are critical for this feature. e MUA recording yields several advantages as compared to sole LFP recordings.
In addition to the higher spatial acuity, the MUA is also known to allow assessing temporal aspects with higher
delity. For example, phase locking to repetitions within the signal above 16 Hz is possible, which is the limit for
LFP frequency following responses45. By this, the MUA is able to reect for example the fundamental frequency
of speech46.
Conclusion
Taken together, the study revealed that the new ECoG grid is a good alternative to conventional PEA considering
the comparable signal quality and the potential to record LFP as well as spatially selective MUA. Additionally,
they provide the advantage of faster sampling from multiple cortical sites simultaneously, without the risk of tis-
sue damage. e recordings can easily be combined with PEA recordings, whenever layer specic information is
needed. e next step would be biocompatibility testing during chronic preparations. Based on the intimate con-
tact to the brain tissue and the stability over several recording sessions, we expect the ECoG grid to be a relevant
tool for chronic recordings in the behaving animal.
Dataset availability. e datasets generated during and/or analyzed during the current study are available
from the corresponding author on reasonable request.
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Acknowledgements
We thank P. Hubka for support with data analysis.
Author Contributions
W.S.K. and U.P.F. designed the ECoG grid. W.S.K., A.K. and P.B. designed the study. W.S.K. and P.B. performed
the experiments and analyzed the data. W.S.K. performed the statistical analysis and P.B. prepared the gures.
W.S.K. draed the manuscript. All authors discussed the data analysis, reviewed and edited the manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-22051-z.
Competing Interests: At the time of the ECoG development, U.P.F. was employed by Blackrock Microsystems
Europe. e authors declare no further conict of interest. is project was supported by the German Federal
Ministry of Education and Research KMU-Innovativ: Medizintechnik FKZ 13GW0050B and by the Deutsche
Forschungsgemeinscha (DFG) EXC 1077/1 Hearing4all. Other than providing nancial support, the funding
sources had no part in the study.
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