Comparison of an Open-hardware Electroencephalography Ampliﬁer

with Medical Grade Device in Brain-computer Interface Applications

Jérémy Frey

1,2

Univ. Bordeaux, Talence, France

Inria, Talence, France

Keywords:

BCI, EEG, Ampliﬁers Comparison, P300 Speller, Workload Classiﬁcation.

Abstract:

Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI

based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applica-

tions, however the dissemination of the technology is limited, in part because of the price of the hardware. In

this paper we compare side by side two EEG ampliﬁers, the consumer grade OpenBCI and the medical grade

g.tec g.USBamp. For this purpose, we employed an original montage, based on the simultaneous recording of

the same set of electrodes. Two set of recordings were performed. During the ﬁrst experiment a simple adapter

with a direct connection between the ampliﬁers and the electrodes was used. Then, in a second experiment,

we attempted to discard any possible interference that one ampliﬁer could cause to the other by adding “ideal”

diodes to the adapter. Both spectral and temporal features were tested – the former with a workload monitoring

task, the latter with an visual P300 speller task. Overall, the results suggest that the OpenBCI board – or a

similar solution based on the Texas Instrument ADS1299 chip – could be an effective alternative to traditional

EEG devices. Even though a medical grade equipment still outperforms the OpenBCI, the latter gives very

close EEG readings, resulting in practice in a classiﬁcation accuracy that may be suitable for popularizing BCI

uses.

1 INTRODUCTION

Brain-computer interfaces (BCI) are communication

devices between humans and machines that rely only

on brain activity (i.e. no muscular input) to issue com-

mands or to monitor states (Wolpaw et al., 2002). BCI

is an emerging research area in Human-Computer

Interaction that offers new opportunities for inter-

action, beyond standard input devices (Tan and Ni-

jholt, 2010). In order to account for brain activity,

portable and non invasive neuroimaging techniques

are most commonly used, such as electroencephalog-

raphy – EEG, which measures electrical current onto

the scalp. The “interface” term covers many differ-

ent areas of applications, for people with or without

disabilities. However, while an increasing number of

systems are being developed, from BCI aimed at con-

trolling a cursor (Wolpaw et al., 2002) to adaptive sys-

tems (Zander and Kothe, 2011), more often than not

the use of the technology is limited.

The price of the hardware is one of the main rea-

sons that prevents the dissemination of non invasive

BCI. Recently, more affordable EEG ampliﬁers ap-

peared on the market, that could solve this issue.

Among them, the OpenBCI board

claims to bring

BCI to the many. Enthusiasts and laboratories have

started to use this board, but the quality of the record-

ings and the reliability of the resulting systems have

yet to be assessed. In this study, we compare side

by side the OpenBCI board with the g.tec g.USBamp

ampliﬁer

, a device commonly used in BCI research.

The price tag of the g.tec solution is around 20 thou-

sands euros, 25 times more expensive than the 800

euros of 16 channels version of the OpenBCI board.

Both OpenBCI and g.USBamp ampliﬁers can record

up to 16 electrodes. This number of channels is suf-

ﬁcient to setup various BCI. We compared OpenBCI

with g.USBamp for, on the one hand, a P300 speller

application and, on the other hand, a workload moni-

toring application. Doing so, we could study respec-

tively temporal and spectral features.

Note that here the question is not to assess which

ampliﬁer is the best device per se. Instead, we inves-

tigate if in a context of popular interactions – a nar-

row scope compared to the possibilities that offers the

http://www.openbci.com/

http://www.gtec.at/

Frey, J.

Comparison of an Open-hardware Electroencephalography Ampliﬁer with Medical Grade Device in Brain-computer Interface Applications.

DOI: 10.5220/0005954501050114

In Proceedings of the 3rd International Conference on Physiological Computing Systems (PhyCS 2016), pages 105-114

ISBN: 978-989-758-197-7

105

g.USBamp – it is conceivable for researchers from the

ﬁeld or (well equipped) enthusiasts to make the leap.

To which extend should we employ devices coming

from the “DIY” (“Do It Yourself”) community for ac-

tual BCI applications?

To answers this question, we adopted an approach

somewhat different to what exists in the literature.

Many papers deal with the comparison of electrodes,

e.g. wet vs dry, with or without a conductive solution.

To do so, authors try to optimize the placement of

both sets of sensors in order to get measures that orig-

inate from the same spots. However, no matter their

efforts they could not merge sensors, and even clever

montages, with electrodes of one sort positioned be-

tween electrodes of the other sort (Tautan et al., 2013),

are not ideal. It will produce a slight offset, hence a

slight inaccuracy. Another alternative is to make sep-

arate measures by repeating the recordings with each

system (Nijboer et al., 2015), but once again the con-

ditions could not be exactly the same.

In the present study we do not attempt to assess

the quality of electrodes, but the behavior of ampli-

ﬁers that are attached to them. Not a whole system,

only the ampliﬁers. Therefore, we would not mind us-

ing the same electrodes during simultaneous record-

ings. This setup would ensure that the signal coming

in each ampliﬁer’s inputs is exactly the same, avoid-

ing any bias regarding the source of the measures.

We made that possible by crafting a dedicated

adapter, one that basically splits in two the electrodes’

wires. Such parallel measurement works because the

ampliﬁers have high impedance circuits, that is to say

that they are designed to not draw any amount of cur-

rent from their source. As such, when one ampliﬁer

is connected, the readings of the other stay the same.

Of course an inﬁnite impedance cannot be achieved,

and no matter the precautions this setup may cause

a very slight difference compared to separate record-

ings. This is why in a second time we added to our

adapter a circuit that prevents any interference be-

tween the two ampliﬁers, using ideal diodes to block

current ﬂows in one direction.

Using two different BCI applications, we inves-

tigated two types of EEG features. A task assess-

ing workload aimed at assessing spectral informa-

tion, and an oddball task sought temporal information.

For each ampliﬁer we measured the performance of

a classiﬁer based on those recordings, and addition-

ally we compared both by correlating the signals that

they recorded. No matter the ﬁnancial aspects, the

qualities of the g.USBamp ampliﬁer make it the per-

fect baseline to gauge new challengers. This is also

true for the electrodes developed by its manufacturer;

in this study we are using g.tec wet and active (pre-

Figure 1: Schematics of the direct adapter between elec-

trodes and ampliﬁers.

ampliﬁed) electrodes.

2 FIRST EXPERIMENT: DIRECT

CONNECTIONS

2.1 Experimental Setup

We acquired 16 EEG channels using the active

g.Ladybird electrodes from g.tec. In this system,

the electrodes are attached to a box that powers

their electrical components and retrieves the signal;

the g.GAMMAbox. After studying the wiring of

the g.GAMMAbox, we designed a printed circuit

board (PCB) to connect both ampliﬁers. Our adapter

plugs on one end to the D-sub 26 connector of the

g.GAMMAbox. Thanks to a pinout composed of

2.54mm connectors that gave access to all the chan-

nels (16 EEG + reference + ground), we attached the

OpenBCI board to the adapter – ground set to “bias”

pin. On the other end of the adapter there was a D-

sub 26 female connector, onto which we could plug

the g.USBamp ampliﬁer as if it were the regular end

of the g.GAMMAbox. The schematics of the 2 layers

PCB is presented in Figure 1.

The EEG channels were positioned according to

the 10-20 system at AFz, Fz, FCz, C3, C1, Cz, C2,

C4, CPz, P3, Pz, P4, POz, O1, Oz and O2 – ground

at FPz, reference on the left earlobe. Since the mea-

sures between both ampliﬁers were identical, only

one recording session occurred, with one participant

– there were no factors to counterbalance with re-

peated measures. The signals of both ampliﬁer were

acquired using OpenViBE 1.0.1

; at 512Hz sampling

rate for the g.USBamp and 125Hz sampling rate for

the OpenBCI board.

http://openvibe.inria.fr/

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

106

The spectral features were investigated with a

workload monitoring application, i.e. a BCI that is

able to discriminate between several levels of mental

effort. The system was trained using the N-back task,

a well-known task to induce workload by playing on

memory load and time pressure (see, e.g. (Mühl et al.,

2014)). The protocol we used was similar to (Mühl

et al., 2014), there were 360 trials presented during 6

blocks of alternate difﬁculty levels (0-back vs 2-back

conditions). The recording session lasted approxi-

mately 12 minutes.

The temporal features were investigated using an

oddball task directly implemented within OpenViBE

with a visual P300 speller. P300 spellers are well-

established BCI applications during which letters that

randomly ﬂash on the screen can be used to spell

words with the sole brain activity. Indeed, when the

letter that the user wants to spell ﬂashes, a particular

event-related potential (ERP) arises within the EEG,

which possess a positive “peak” around t=300 ms af-

ter the stimulus onset. This is commonly referred to

as the “oddball paradigm” since the occurrence of rare

stimuli is used to elicit brain responses. During the

recordings a matrix of 6 by 6 letters and digits was

displayed in full screen on a 24-inch display. Only the

calibration session occurred, during which one need

to focus one’s attention on a predeﬁned sequence of

letters. 32 letters composing a pangram were men-

tally “spelled” this way. The sentence was, without

spaces, “pack my box with ﬁve dozen liquor jugs”.

Letters were ﬂashing for 0.2s. There were 24 ﬂashes

per letter (12 times the row, 12 times the column),

hence due to the matrix disposition there were in total

4608 trials, among which 768 were targets – “odd”

trials, i.e. the letters of the target sentence were ﬂash-

ing. The recording session lasted approximately 30

minutes.

The acquisition of both ampliﬁers’ signals and

the P300 application occurred within the same Open-

ViBE scenario (script). The recordings of each am-

pliﬁer were synchronized with the appropriate events

and exported in separate GDF ﬁles for later analyses.

There was also only one scenario involved in the syn-

chronization of all signals and events in the case of the

N-back task; stimulation from the python script sup-

porting this latter task were retrieved using the LSL

protocol, a network protocol dedicated to physiologi-

cal recordings which ensures accurate timings

2.2 Signal Processing

Two kinds of analyses were performed, using stan-

dard BCI signal processing pipelines. One aimed at

https://github.com/sccn/labstreaminglayer

assessing if and how the ampliﬁers differ in practice,

when used for classiﬁcation. The second then looked

at the correlation between the acquired signals.

2.2.1 Classiﬁcation

The signal processing of the data acquired during the

N-back task is analogous to (Mühl et al., 2014), i.e. 2s

time windows, 5 frequency bands – delta (1-3 Hz),

theta (4-6 Hz), alpha (7-13 Hz), beta (14-25 Hz) and

gamma (26-40 Hz) – and spatial ﬁlters. We used

common spatial patterns spatial ﬁlters to reduce the

16 channels to 6 “virtual” channels more discrimi-

nant between the workload conditions – see (Ramoser

et al., 2000). Additionally, we also tested a 3 fre-

quency bands version of our pipeline, that consider

only the lower frequencies, less prone to muscular ar-

tifacts – delta, theta and alpha.

Concerning the oddball task, we band-passed the

signal between 0.5Hz and 40Hz, downsampled it by a

factor 32 using the “decimate” Matlab function – by

a factor 8 for OpenBCI because of the reduced sam-

pling rate –, and applied regularized Eigen Fisher spa-

tial ﬁlters – a spatial ﬁlter speciﬁcally designed for

ERPs classiﬁcation (Hoffmann et al., 2006) – to re-

duce channels’ dimension from 16 to 5. We used 1s

time windows after stimuli onsets – letters’ ﬂashes –

to epoch (“slice”) our signal. However, in order to

prevent data to overlap between consecutive stimuli

due to the rapid pace of the ﬂashes, after a ﬁrst pass

of epoching we discarded overlapping time windows

from further analyses. This ensured that no part of the

signal could be seen twice by the classiﬁer between

the training phase and the testing phase and bias the

accuracy. The procedure was automatic, the ﬁrst non-

overlapping epoch in order of appearance being kept.

As a result, in the end we obtained 48 target trials

and 240 distractor trials for classiﬁcation, identical

between the g.USBamp and the OpenBCI recordings.

Both for the workload and the P300 speller tasks,

we used shrinkage LDA (linear discriminant analysis)

for classiﬁcation (Ledoit and Wolf, 2004). To assess

the classiﬁers’ performance on the calibration data,

we used 4-fold cross-validation. I.e. we split the col-

lected data into 4 parts of equal size, used 3 parts to

calibrate the classiﬁers and tested the resulting clas-

siﬁers on the unseen data from the remaining part.

This process occurred 3 more times so that in the end

each part was used once as test data. Finally, we aver-

aged the obtained classiﬁcation accuracies. The accu-

racy was measured using the area under the receiver-

operating characteristic curve (AUROCC). The AU-

ROCC is a metric that is robust against unbalanced

classes, as it is the case with oddball tasks. A score

of “1” means a perfect classiﬁcation, a score of “0.5”

Comparison of an Open-hardware Electroencephalography Ampliﬁer with Medical Grade Device in Brain-computer Interface Applications

107

is chance. In order to make statistical comparisons

between both ampliﬁers for each type of features that

we studied, we ran 10 times the analyses – the trials

were selected randomly for cross-validation.

2.2.2 Correlations

We compared, on the one hand, the frequency spectra

associated to the different workload conditions and,

on the other hand, the time course of the ERP that

were caused by the ﬂashing target letters. To do so,

we used Pearson correlations, on par with the litera-

ture for similar analyses – e.g. (Zander et al., 2011).

In order to ensure a 1-to-1 correspondence between

our sets of data, the recordings from the g.USBamp

were downsampled to 125Hz – same sampling rate

as for the OpenBCI – using the “resample” function

from Matlab R2014a signal processing toolbox.

Concerning the workload task, we ﬁrst aggregated

the 2s time-windows corresponding to each condition

(0-back and 2-back). Then we used the “spectopo”

function of the EEGLAB toolbox (version 13.4.4b)

to compute the grand average power spectral between

1Hz and 40Hz, for each channel. The output of

the function was then passed on to R (version 3.0.2)

to compute correlations through the “rcorr” function

from the “Hmisc” package.

For the oddball task, we ﬁrst band-passed the sig-

nals between 1Hz and 8Hz – the approximate fre-

quency band used for classiﬁcation. Then we ex-

tracted time epochs starting 0.5s prior to the ﬂashing

of the target letters and ending 1s after stimuli on-

set. Contrary to what occurred for classiﬁcation, we

did not prune overlapping epochs in the oddball task

when we compute the averaged ERP – there was no

bias that could have been induced here. Finally, we

averaged the ERP per channel before exporting the

time points to the R environment.

2.3 Results

2.3.1 Classiﬁcation

The results regarding classiﬁcation accuracy are pre-

sented in Table 1, with the AUROCC scores for each

one of the 10 repetitions, for both ampliﬁers and both

tasks – including the 3 and 5 frequency bands pipeline

for workload.

We tested for signiﬁcance using Wilcoxon signed-

rank tests. There was a signiﬁcant difference be-

tween ampliﬁers for the P300 tasks (p < 0.01). The

AUROCC mean score for the g.USBamp was 0.961

vs 0.918 for the OpenBCI. There were however no

signiﬁcance but tendencies concerning the workload

task, with mean AUROCC scores between 0.85 and

0.86 for the 3 bands pipeline (p = 0.095) and between

0.89 and 0.90 for the 5 bands task (p = 0.079) – see

Table 1 for details.

2.3.2 Correlations

When we ﬁrst analyzed our data to seek correlations

regarding the oddball tasks, we realized that a shift

occurred during the recordings, as denoted in Figure 2

by the grand average of the ERP for target trials across

channels. This may have been caused by a software

issue (see Discussion). In order to correct the shift and

conduct proper comparisons between both ampliﬁers’

measures, we used a cross-correlation to estimate the

time shift, using the “ccf” function from the R “stats”

package. We found a delay of 88ms between the two

signals – 11 data points at 125Hz, see Figure 2.

In Figure 3a, the averaged ERP were shifted by as

much for each channel. Corresponding Pearson corre-

lation R scores, that were computed using the “rcorr”

function, are presented in Table 2. The mean R score

is 0.9965 and is statistically signiﬁcant (p < 0.001).

There was also a signiﬁcant correlation

(p < 0.001) for the spectral features, with a mean R

score of 0.9983 for the 0-back condition and 0.9979

the 2-back condition (see Table 2 for details). Among

the brain signals patterns that could be expected

during the completion of a difﬁcult task, the decrease

nearby the alpha frequency band during the 2-task

condition can be observed within per-channel spectra

presented in Figures 3b and 3c. Note that we did not

correct time shifts prior to workload analyses due

to the nature of the features – i.e. spectral and not

temporal.

2.4 Discussion

The correlation between both temporal and spectral

features tends so show that the signals acquired by

the g.USBamp and the OpenBCI are, if not identical,

very closely related. For every condition and channel

tested, the Pearson R score was greater than 0.99.

There were however more dissimilarities in the

classiﬁcation accuracy obtained during the corre-

sponding tasks. While there were hardly a difference

between the AUROCC scores computed from both

ampliﬁers with the N-back tasks, the g.USBamp per-

formed signiﬁcantly better than the OpenBCI during

the P300 speller task. The time shift observed after-

wards between the two ampliﬁers may partially ex-

plain this difference. Indeed, the detection of ERP is

particularly sensitive to signals’ latency, and a shift

between events’ timestamp and signal’s acquisition

could result in such degradation of performance when

temporal features are involved.

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108

−1

−0.5 0.0 0.5 1.0

Time (s)

Device gtec openbci

ERP for p300 − target − direct connection

−30 −20 −10 0 10 20 30

0.0 0.2 0.4 0.6 0.8 1.0

Lag

CCF

ERP cross−correlation − direct connection

Figure 2: Left: Averaged ERP across channels of the target trials during the oddball task, before time shift correction. Right:

Cross-correlation between the ampliﬁers. The computed lag of 11 data points corresponds to 88ms. (Direct connection.)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−2

−1

−3

−2

−1

−2

−1

−3

−2

−1

−3

−2

−1

−2

−1

−2

−1

−2

−1

−3

−2

−1

−2

−1

−2

−1

−2

−1

0.0 0.5 1.0 0.0 0.5 1.0 0.0 0.5 1.0 0.0 0.5 1.0

Time (s)

Device gtec openbci

ERP for p300 − target − direct connection

(a)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−10

0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40

Frequency (Hz)

Power 10*log

(uV

/Hz)

Device gtec openbci

Spectra for workload − 0−back − direct connection

(b)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−10

0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40

Frequency (Hz)

Power 10*log

(uV

/Hz)

Device gtec openbci

Spectra for workload − 2−back − direct connection

(c)

Figure 3: Direct connection: averaged ERP for the target trials of the oddball task (a), averaged spectra for the 0-back (b) and

2-back (c) trials of the N-bak task.

Comparison of an Open-hardware Electroencephalography Ampliﬁer with Medical Grade Device in Brain-computer Interface Applications

109

Table 1: Classiﬁcation accuracy (AUROCC scores) for the P300 and workload tasks studied during the ﬁrst experiment

– direct connection between the electrodes and the ampliﬁers. The 4-fold cross validations were repeated 10 times. Two

pipelines are presented for the workload: 3 frequency bands (“WL 3b”, δ+ θ+ α) as well as 5 frequency bands pipeline (“WL

5b”, δ + θ + α+ β + γ). Signiﬁcance was tested using Wilcoxon signed-rank tests.

Condition Ampliﬁer 1 2 3 4 5 6 7 8 9 10 Mean SD

P300 g.USBamp 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.97 0.961 0.003

OpenBCI 0.92 0.92 0.91 0.91 0.92 0.92 0.93 0.92 0.92 0.91 0.918 0.006

WL 3b g.USBamp 0.85 0.85 0.85 0.86 0.85 0.87 0.87 0.87 0.87 0.86 0.860 0.009

OpenBCI 0.86 0.86 0.85 0.86 0.84 0.86 0.85 0.85 0.85 0.85 0.853 0.007

WL 5b g.USBamp 0.90 0.89 0.90 0.89 0.91 0.90 0.89 0.90 0.89 0.90 0.897 0.007

OpenBCI 0.91 0.89 0.87 0.88 0.89 0.88 0.89 0.89 0.89 0.90 0.889 0.011

Table 2: Pearson correlation R scores between g.USBamp and OpenBCI recordings at the 16 different electrode locations

with a direct connection. The “P300 target” condition corresponds to temporal features (ERP averaged across trials) and the

workload conditions to spectral features.

AFz Fz FCz C3 C1 Cz C2 C4 CPz

P300 target 0.998 0.997 0.997 0.997 0.997 0.994 0.996 0.992 0.994

Workload 0-back 0.999 0.999 0.998 0.998 0.998 0.998 0.998 0.999 0.998

Workload 2-back 0.999 0.999 0.998 0.998 0.998 0.998 0.998 0.999 0.997

P3 Pz P4 POz O1 Oz O2 Mean SD

P300 target 0.995 0.994 0.996 0.996 0.996 0.994 0.995 0.9965 0.0015

Workload 0-back 0.998 0.998 0.998 0.999 0.998 0.998 0.998 0.9983 0.0003

Workload 2-back 0.998 0.998 0.998 0.998 0.997 0.998 0.998 0.9979 0.0005

The radio transmission between the wireless

OpenBCI board and the dongle plugged to the com-

puter may be one of the cause of the situation. The

problem could also originate from the software. As

a matter of fact, the OpenViBE acquisition driver of

the OpenBCI board was released no so long before

our experiment, and was still labelled as “unstable”

as for version 1.0.1 of the software. One “oddity”

that may further highlight the youth of OpenBCI soft-

ware integration: we realized during our analysis that

the recorded signals were completely inverted on the

Y axis. The voltage reported by the board were the

opposite of what g.USBamp was claiming. Since on

numerous occasions we acknowledged the accuracy

of g.tec devices readings, it is the OpenBCI’s signals

that we inverted back to “normal” prior to correlation

analyses.

Beside time shifts issues, as mentioned during the

introduction we needed to strengthen those ﬁrst in-

sights by discarding the eventuality that both EEG

signals may have inﬂuence each other due to the di-

rect wiring with the electrodes.

3 SECOND EXPERIMENT:

ISOLATED CONNECTIONS

The second set of recordings is very similar to the

what was described during the ﬁrst study. The second

experiment only differs by the nature of the adapter

that was employed. As such we will only discuss the

changes that were made to the hardware and quickly

dive into the results.

3.1 Ideal Adapter

We modiﬁed the adapter that connects the ampliﬁers

to the g.GAMMAbox – and by extent to the EEG

electrodes. Instead of a direct connection between

each ampliﬁer’s inputs and the EEG channels, we in-

terposed “ideal” (or “super”) diodes on the branches

of the “Y” wiring.

Diodes are electrical components that let the cur-

rent ﬂow in only one direction, the “forward” direc-

tion. Hence, this type of montage ensures that no

current could travel directly from one ampliﬁer to the

other, contaminating the recordings. However, reg-

ular diodes cause a voltage drop. The voltage drop

varies depending on the materials used for their con-

struction, but it is at least 0.3V. Meaning that if the

current coming in the forward direction is lesser than

0.3V, no signal will pass through. 0.3V is an order of

magnitude superior to the range of EEG signal – ap-

proximately a thousand time, therefore regular diode

could not be used.

To circumvent this problem, we utilized a particu-

lar montage that involved operational ampliﬁers (op-

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110

Figure 4: Schematics of the adapter with the ideal diodes

montage. Note that there is a set of ideal diodes on the

ground channel, but they were shorted with a jumper during

our experiment.

amp). Op-amps are components widely used in elec-

trical circuits, acting as sorts of “building blocks”.

Notably, in combination with a regular diode, one

could use a precision rectiﬁer conﬁguration to ob-

tain an “ideal” diode. This particular montage is also

known as a “super” diode, since there will always be

a slight voltage drop, but in this case, thanks to the

gain of the op-amp, it becomes negligible.

We mounted 36 of such ideal diodes on the

adapter. One on each end of the “Y” section associ-

ated to the 16 EEG channels, plus 2 for the reference.

Due to the nature of the electrical recordings, only

the ground was left without such circuit. We utilized

Texas Instrument op-amps, model TLC2272ACPE4.

The TLC227xA series are more indicated for preci-

sion application, and with 2 op-amps per chip we

could limit the overall size of the adapter. The op-

erational ampliﬁers were powered by an external cir-

cuit with regulated -2.5 / +2.5 voltage. The schemat-

ics of the adapter – also a 2 layers 2 layers PCB –

is presented in Figure 4. The ideal diodes montage,

placed before ampliﬁers’ inputs, prevented any cur-

rent to ﬂow in reverse direction from either ampliﬁer

to the adapter; it ensured that one set of recordings

would not bias the other. One recording session oc-

curred for each application and each condition.

3.2 Results

The signal processing and the analyses were strictly

identical to the ﬁrst experiment detailed above, refer

to the previous section for related information.

3.2.1 Classiﬁcation

As with the ﬁrst study, the results regarding classiﬁca-

tion accuracy are presented in Table 3, with the AU-

ROCC scores for each one of the 10 repetitions, for

both ampliﬁers and both tasks – including the 3 and

5 frequency bands pipeline for workload. We tested

for signiﬁcance using Wilcoxon signed-rank tests. No

matter the task there was no signiﬁcant difference, al-

though the 5% threshold was nearly reached for spec-

tral features – p-value was 0.157 for the P300 task,

0.051 for the 3 bands version of the workload pipeline

and 0.286 for the 5 bands version.

3.2.2 Correlations

Concerning the P300 oddball task, there was a offset

of 88ms as well between the recordings of both ampli-

ﬁer with the isolated connection – see Figure 5 for the

grand ERP average and the cross correlation. The per-

channel averaged ERP are plotted in ﬁgure 6a. Corre-

sponding Pearson correlation R scores are presented

in Table 4. The mean R score is 0.8847 and is statis-

tically signiﬁcant (p < 0.001).

There was also a signiﬁcant correlation

(p < 0.001) for the spectral features, with a

mean R score of 0.9976 for the 0-back condition

and 0.9987 the 2-back condition (see Table 4 for

details). The per-channel spectra are presented in

Figures 6b and 6c. As with the direct connection, the

band frequency changes between the 0-back and the

2-back conditions can be observed in the spectra.

3.3 Discussion

The results with the isolated connections are not that

different from what was obtained during the ﬁrst ex-

periment. This would suggest that directly connecting

two high impedance ampliﬁers to the same EEG elec-

trodes could be a viable montage for a side-by-side

comparison.

Since with both types of connector there was only

one set of recordings, we could not draw any conclu-

sion about the lower classiﬁcation accuracy obtained

with the isolated montage. The vigilance level of the

participant alone could explain these performances.

Thanks to signals’ correlations, however, we may

infer that noise was added to the system due to

the presence of additional electrical components in

the adapter. Indeed, while the spectra were once

again strongly correlated, the averaged ERP achieved

“only” a mean R score of 0.88. Here external factors

such as the metal state of the participant or the qual-

ity of electrodes contacts could not have inﬂuenced

one ampliﬁer rather than the other. Since temporal

features are more sensitive than spectral features to

signal quality – e.g. one “peak” in the signal vs os-

cillatory patterns over several seconds –, it is instead

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111

−1

−0.5 0.0 0.5 1.0

Time (s)

Device gtec openbci

ERP for p300 − target − isolated connection

−30 −20 −10 0 10 20 30

−0.2 0.0 0.2 0.4 0.6 0.8

Lag

CCF

ERP cross−correlation − isolated connection

Figure 5: Left: Averaged ERP across channels of the target trials during the oddball task, before time shift correction. Right:

Cross-correlation between the ampliﬁers. The computed lag of 11 data points corresponds to 88ms. (Isolated connection.)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−2.5

0.0

2.5

−2

−1

−2

−1

−2

−1

−2

−1

−3

−2

−1

−2

−1

−2

−5

−2

−1

0.0 0.5 1.0 0.0 0.5 1.0 0.0 0.5 1.0 0.0 0.5 1.0

Time (s)

Device gtec openbci

ERP for p300 − target − isolated connection

(a)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−10

0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40

Frequency (Hz)

Power 10*log

(uV

/Hz)

Device gtec openbci

Spectra for workload − 0−back − isolated connection

(b)

AFz Fz FCz C3

C1 Cz C2 C4

CPz P3 Pz P4

POz O1 Oz O2

−10

0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40

Frequency (Hz)

Power 10*log

(uV

/Hz)

Device gtec openbci

Spectra for workload − 2−back − isolated connection

(c)

Figure 6: Isolated connection: averaged ERP for the target trials of the oddball task (a), averaged spectra for the 0-back (b)

and 2-back (c) trials of the N-bak task.

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112

Table 3: Classiﬁcation accuracy (AUROCC scores) for the P300 and workload tasks studied during the second experiment

– isolated connection between the electrodes and the ampliﬁers. The 4-fold cross validations were repeated 10 times. Two

pipelines are presented for the workload: 3 frequency bands (“WL 3b”, δ+ θ + α) as well as 5 frequency bands pipeline (“WL

5b”, δ + θ + α + β + γ). Signiﬁcance was tested using Wilcoxon signed-rank tests.

Condition Ampliﬁer 1 2 3 4 5 6 7 8 9 10 Mean SD

P300 g.USBamp 0.84 0.82 0.83 0.83 0.81 0.85 0.83 0.84 0.83 0.84 0.832 0.011

OpenBCI 0.82 0.83 0.83 0.82 0.81 0.82 0.84 0.82 0.83 0.83 0.825 0.008

WL 3b g.USBamp 0.88 0.88 0.88 0.91 0.89 0.90 0.89 0.88 0.89 0.88 0.888 0.100

OpenBCI 0.88 0.88 0.89 0.89 0.88 0.89 0.87 0.88 0.88 0.87 0.881 0.007

WL 5b g.USBamp 0.92 0.91 0.92 0.90 0.91 0.89 0.90 0.91 0.91 0.90 0.907 0.009

OpenBCI 0.90 0.92 0.92 0.91 0.92 0.90 0.91 0.91 0.90 0.91 0.910 0.008

Table 4: Pearson correlation R scores between g.USBamp and OpenBCI recordings at the 16 different electrode locations

with an isolated connection. The “P300 target” condition corresponds to temporal features (ERP averaged across trials) and

the workload conditions to spectral features.

AFz Fz FCz C3 C1 Cz C2 C4 CPz

P300 target 0.976 0.934 0.892 0.846 0.872 0.881 0.838 0.811 0.912

Workload 0-back 0.999 0.998 0.998 0.997 0.998 0.998 0.998 0.996 0.998

Workload 2-back 0.999 0.999 0.999 0.998 0.998 0.999 0.999 0.999 0.999

P3 Pz P4 POz O1 Oz O2 Mean SD

P300 target 0.849 0.823 0.896 0.910 0.878 0.982 0.853 0.8847 0.0483

Workload 0-back 0.998 0.998 0.998 0.997 0.997 0.997 0.997 0.9976 0.0007

Workload 2-back 0.999 0.999 0.999 0.999 0.999 0.998 0.998 0.9987 0.0004

more plausible that the difference with the ﬁrst exper-

iment comes from the adapter.

Nonetheless, even though the ideal diode montage

did not produce ideal signals, those results still ad-

vocate for a close proximity between the g.USBamp

and the OpenBCI. No device behaved “better” than

the other, because no matter the lower correlation be-

tween averaged ERP, the classiﬁcation accuracy is in

practice comparable between both ampliﬁers. Each

one probably endured different ﬂuctuations since each

had a dedicated set of ideal diodes.

4 CONCLUSION

During this preliminary study, we compared the

OpenBCI board to the g.tec g.USBamp ampliﬁer. We

employed an original montage, based on the simulta-

neous recording of the same set of electrodes. While

as a ﬁrst approach we used a simple adapter with a di-

rect connection between the ampliﬁers and the elec-

trodes, in a second experiment we attempted to dis-

card any possible interference that one ampliﬁer could

cause to the other.

To do so, we built an adapter that embedded

“ideal” diodes, components that prevented electrical

currents to ﬂow “backward”. This ensured that we

could test both devices in isolation. We did not try

to compare both adapters as the purpose was simply

to gather more insights about the possibility of simul-

taneous recordings – this was a precaution to detect

a possible bias. For all applications and conditions

AUROCC scores were far beyond chancel level; the

OpenBCI ampliﬁer came close to the g.USBamp in

terms EEG features and effective performance.

That is not to say that the OpenBCI could replace

an equipment such as the g.USBamp, though. For in-

stance, this open-hardware initiative does not aim at

medical applications, hence it should be employed in

sensitive contexts. It does not possess any certiﬁca-

tion; one reason why so many cheap EEG device are

wireless is not for practicality, but to avoid any hazard

due to power supply. Connecting somehow a body to

the power grid requires extra precautions and a certi-

ﬁed isolation, moreover when the impedance between

the electrodes and the brain is intentionally lowered.

There are also few issues with the current state of

the OpenBCI project. One concerns the sampling rate

of the board. While 125Hz may be enough for our

use-cases – no frequencies beyond 40Hz were used

during the work presented here – it may not sufﬁce

others. The limitation of the sampling rate is caused

by the wireless protocol used for data transmission.

OpenBCI can deliver up to 250Hz signals to the com-

puter, but only on 8 channels instead of 16. Note that

this may be optimized in the future by updating the

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113

ﬁrmware or using alternate communications – as far

as the board itself is concerned, the documentation of

the ADS1299 chip ensuring analog-to-digital conver-

sion claims a sampling rate up to 16,000Hz.

With those limitations in mind, overall the results

suggest that the OpenBCI board – or a similar solution

based also on the Texas Instrument ADS1299 chip –

could be an alternative to traditional EEG ampliﬁers.

Even though medical grade equipment possesses cer-

tiﬁcation and still outperforms the OpenBCI board in

terms of classiﬁcation, the latter gives very close EEG

readings. In practice, the obtained classiﬁcation accu-

racy may be suitable for reliable BCI in popular set-

tings, widening the realm of applications and increas-

ing the number of potential users.

ACKNOWLEDGEMENTS

I thank Thibault Laine for his technical assistance dur-

ing this project.

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