Open Ephys electroencephalography (Open Ephys + EEG): a modular, low-cost, open-source solution to human neural recording

Abstract

Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys + EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys + EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8–14 Hz activity between the Open Ephys + EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys + EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

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Journal of Neural Engineering
PAPER
Open Ephys electroencephalography (Open
Ephys + EEG): a modular, low-cost, open-source
solution to human neural recording
To cite this article: Christopher Black et al 2017 J. Neural Eng. 14 035002
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1 © 2017 IOP Publishing Ltd Printed in the UK
Journal of Neural Engineering
Open Ephys electroencephalography
(Open Ephys + EEG): a modular, low-cost,
open-source solution to human neural
recording
ChristopherBlack1, JakobVoigts2,3, UdayAgrawal2, MaxLadow2,
JuanSantoyo2, ChristopherMoore2 and StephanieJones2
1 Center for Biomedical Engineering, Brown University, Providence, RI, United States of America
2 Department of Neuroscience, Brown University, Providence, RI, United States of America
3 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA,
United States of America
E-mail: christopher_black@brown.edu and jvoigts@mit.edu
Received 17 October 2016, revised 1 March 2017
Accepted for publication 7 March 2017
Published 30 March 2017
Abstract
Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural
activity non-invasively with millisecond resolution using minimal equipment in or outside of
a lab setting. EEG can be combined with a number of techniques for closed-loop experiments,
where external devices are driven by specic neural signals. However, reliable, commercially
available EEG systems are expensive, often making them impractical for individual use and
research development. Moreover, by design, a majority of these systems cannot be easily
altered to the specication needed by the end user. We focused on mitigating these issues by
implementing open-source tools to develop a new EEG platform to drive down research costs
and promote collaboration and innovation. Approach. Here, we present methods to expand
the open-source electrophysiology system, Open Ephys (www.openephys.org), to include
human EEG recordings. We describe the equipment and protocol necessary to interface
various EEG caps with the Open Ephys acquisition board, and detail methods for processing
data. We present applications of Open Ephys + EEG as a research tool and discuss how this
innovative EEG technology lays a framework for improved closed-loop paradigms and novel
brain-computer interface experiments. Main results. The Open Ephys + EEG system can
record reliable human EEG data, as well as human EMG data. A side-by-side comparison of
eyes closed 8–14 Hz activity between the Open Ephys + EEG system and the Brainvision
ActiCHamp EEG system showed similar average power and signal to noise. Signicance.
Open Ephys + EEG enables users to acquire high-quality human EEG data comparable to
that of commercially available systems, while maintaining the price point and extensibility
inherent to open-source systems.
Keywords: open source, EEG, electrophysiology, Open Ephys, low-cost
(Some guresmay appear in colour only in the online journal)
C Black etal
Printed in the UK
035002
JNEIEZ
© 2017 IOP Publishing Ltd
14
J. Neural Eng.
JNE
1741-2552
10.1088/1741-2552/aa651f
Paper
3
Journal of Neural Engineering
IOP
2017
1741-2552/17/035002+14$33.00
https://doi.org/10.1088/1741-2552/aa651f
J. Neural Eng. 14 (2017) 035002 (14pp)

C Black etal
2
Introduction
Since its inception in the 1920s by Hans Berger, electroen-
cephalography (EEG) has been an integral tool in the study of
human neural activity (Berger 1935). Electrodes placed on the
scalp monitor small changes in electric potential (microvolt
scale) created by the synchronous activation of 10–20 cm2 of
cortical tissue (Nunez and Srinivasan 2006). While its spatial
resolution is limited, EEG can register oscillations that occur
at frequencies as high as 500 Hz, known as fast ripples, (Baker
etal 2003) or as slow as sub-1 Hz, known as slow wave oscil-
lations (Marshall et al 2006, Nunez and Srinivasan 2006).
EEG has a wide variety of applications from its role as a diag-
nostic tool in clinical conditions such as Parkinson’s Disease
(Soikkeli etal 1991) and epilepsy (Gotman 1982), to studying
cognitive processes such as memory (Klimesch 1999) and
attention (Worden etal 2000), to unraveling the mechanisms
of transcranial electric stimulation for neural modulation
(Helfrich etal 2014), to being used in brain-comp uter inter-
face (Farwell and Donchin 1988) (BCI) technologies for the
operation of prosthetics (Guger et al 1999) and computers
(Wolpaw etal 1991).
The utility of EEG, however, is masked by the tools pro-
vided by most commercial EEG manufacturers. Current com-
mercial EEG systems can cost upwards of $60 000 dollars or
more; for example, the BioSemi system (www.biosemi.com/
faq/prices.htm) costs €21 000 ($22 662.15) for a 32 channel
system and €44 000 for a 128 channel system ($47 482.60),
while the Brainvision actiCHamp system (www.brainvision.
com/actichamp.html) costs $28 460 for a 32 channel system
and $64 000 for a 128 channel system. These high-cost sys-
tems are problematic for small-scale academic and teaching
settings where limited resources often dictate the freedom
of purchasing and work-ow. More often than not these sys-
tems only allow for passive data acquisition; meaning signals
are collected and analyzed off-line. It is possible, however,
to implement software packages to utilize these commercial
systems in closed-loop BCI control as well as neurofeedback
applications. For example, the BCI2000 system is an integra-
tive software package used for BCI research that interfaces
with data acquisition systems to collect and process data on-
line, and allow for control of external devices (Schalk et al
2004). Despite this, proprietary hardware and software still
create a large barrier for researchers to tailor EEG systems to
t their research. However, in the past decade, open-source
information sharing has galvanized the production of low-cost,
easily accessible, human-based neurotechnologies that include
EEG. For example, the OpenBCI system (www.openbci.com/)
is a popular, modular, open-source tool for recording human
EEG. OpenBCI has several, wireless systems; from 4-channels
($199), to 8-channels ($499), to 16-channels ($949.99).
Additionally, the system can be linked so a 32-channel system
would be the price of two 16-channel systems ($1899.98).
They have also developed an open-source EEG headset, the
Ultracortex ($249.99–$349.99). To add to their low-cost tech,
they also have extensive documentation to help users learn
more about the equipment they are using. In the event that the
documentation is not enough, there is a large user community
where individuals can ask questions, post messages about their
research, and even take part in or create scientic challenges.
Backyard brains, an open-source company geared towards
making neuroscience research accessible to everyone, has a
one-channel EEG system ($149.99) along with a host of other
EMG and neural recording tools. As with OpenBCI, Backyard
brains also has quite a bit of documentation that ranges from
general user support to experimental instructions on how to
control a robotic arm through EMG. They even have a page
dedicated to the ethics of their work, as well as a blog for
posting updates. OpenViBE is another company that develops
software for real-time data analysis as well as BCI develop-
ment (Renard etal 2010), and BCI2000, alluded to earlier, is
also an open community that provides their software and doc-
umentation for free (www.schalklab.org/research/bci2000).
These projects have not only pioneered human EEG research,
but they continue to develop new and unique tools for moving
neuroscience forward.
To continue the growth of the open-source technologies we
have developed a new EEG system built on the Open Ephys
platform (www.open-ephys.org). To date, Open Ephys has
been developed and used for extracellular recordings with tet-
rodes (Gray etal 1995) in rodents (Siegle and Wilson 2014)
and Pogona dragons (Shein-Idelson etal 2016). In using this
system, we build on the versatility of Open Ephys so that a
single system can not only record extracellular potentials in
animals, but also scalp potentials in humans. Here, we present
detailed methods to expand Open Ephys’s utility to include
these human EEG recordings, and show several examples of
its use and comparison to a standard EEG system. Lastly, we
discuss future research directions, including application to
closed-loop experiments.
1. Adapting Open Ephys to EEG: components
andassembly
All EEG systems are composed of an electrode cap, digital
ampliers, a data acquisition system, and a computer. The
electrode cap is used to pick up small electric potentials on
the scalp, the digital ampliers amplify the incoming small
signals to be read out by the computer, the data acquisition
system registers the incoming neural data and tags it with a
time stamp, and the computer allows for visualization, data
storage, and data analysis. Our Open Ephys + EEG system
includes each of these components. In this sectionwe describe
the exact components used and associated current day costs
(section 1.1, table 1), and methods for assembly with esti-
mated assembly time (section 1.2).
1.1. Open Ephys + EEG components and costs
The rst piece of our system is an electrode cap (gure 1(a)).
Electrode caps are available commercially and can have a
range of characteristics. Electrode caps can consist of just
a ground, a reference, and a signal electrode, or they can
be made of high density arrays with upwards of 500 elec-
trodes. They can also record through different mediums; wet
J. Neural Eng. 14 (20 17) 035002

C Black etal
3
caps consist of a metal electrode that conducts ohmic cur-
rent from the scalp via a conductive gel, whereas dry caps
pick up scalp currents from a capacitive link. Additionally,
caps can be passive, meaning signals picked up from elec-
trodes are sent directly to neural recording ampliers, or they
can be active, meaning there is a pre-amplication step for
eliminated environ mental noise before the signal is recorded.
These different electrode cap features change the price drasti-
cally and have different implications for a given experiment.
Fortunately, any commercially available electrode cap can be
used for the Open Ephys + EEG system provided an appro-
priate connector for the cap can be acquired and the pin layout
is known. Table1 includes the price range for a lower end cap;
the passive, wet, 32-channel EasyCap, to a higher end cap; the
active, wet, 64-channel BrainVision ActiCap.
In order to transfer neural signals from the electrode cap
to a computer, the EEG cap must rst be connected directly
into an Open Ephys + EEG breakout board (gure 1(b)). This
breakout board is the intermediate piece that enables us to
interface the EEG cap to the Open Ephys ampliers (gure
1(c)), so that EEG signals can be amplied and sent to the
Open Ephys acquisition board (gure 1(d)), which converts
the signals to a format that can be read by a PC (gure 1(e)).
Specically, a fully assembled Open Ephys + EEG breakout
board, as shown in gures1(b) and 2, has four male Pak-50
connectors for connecting up to four 32-channel EEG inputs
(128-channels of EEG data in total). These connectors are
relatively inexpensive, currently costing under $10 each
(table 1). The four Pak-50 connectors are linked via conduc-
tive traces to four female Omnetics NPD-36-VV-GS connec-
tors (see gure 2(b)) that can be directly connected to four
separate Open Ephys ampliers (gure 1(c)). Currently, the
Omnetics connectors can only be purchased in orders of four
or more (table 1).
To help with data quality and analysis in EEG, it is often
necessary to change the reference point or to include an
Table 1. List of parts, manufacturers, and costs, for both 32 channel, and 128 channel Open Ephys EEG system to date (February 2017).
Component Company Vendor Site Cost
EEG electrode cap
EasyCap, ActiCap EasyCap, Brainvision www.easycap.com,
www.brainvision.com
$1900.00–$16 040.00
EEG breakout board
Breakout board Open Ephys www.seeedstudio.com $20.00
Nano strip connector NPD-36-VV-GS Omnetics Connector Corporation www.omnetics.com $62.16–$248.64 (×4)
Headers (3-pin) Harwin Inc. www.digikey.com $1.36 (×8)
Headers (8-pin) Harwin Inc. www.digikey.com $0.90 (×2)
Pak-50 connectors 3M www.digikey.com $6.55–$26.20 (×4)
Amplier
32 channel Headstage Intan Technologies www.intantech.com $995.00–$3980 (×4)
SPI Cable Intan Technologies www.intantech.com $295.00–$1180 (×4)
Data Acquisition
Acquisition Board Open Ephys www.labmaker.org $2350.00
Computer (Ideacenter AI0) Lenovo www.lenovo.com $1400.00
Total: $7060.97–$25 247.10
Figure 1. Schematic for Open Ephys with EEG connectivity. (a) Scalp potentials are registered by an electrode cap, which are then sent
to (b) the EEG breakout board that interfaces with (c) Intan Amplier based headstages. Amplied signals are sent via an SPI cable to the
(d)Acquisition Board and are then sent via USB to (e) a PC for visualization and data storage.
J. Neural Eng. 14 (20 17) 035002

C Black etal
4
additional modality of recording to track sources of biological
noise such as electrooculography (EOG) or electromyography
(EMG). The Open Ephys + EEG breakout board shown in
gures1(b) and 2 includes twelve series of headers, or pins,
to allow for re-sourcing amplier inputs from EEG to an addi-
tional electrophysiological signal such as an EMG or EOG,
and for re-referencing signals on the y. This can be done
using a series of jumper connectors to create an electrical con-
nection between two header pins.
The reference pin of each Omnetics connector is con-
nected to a header pin on the EEG breakout board (gure
2(a), orange, far right). Figure2(b) depicts the ‘default’ mode
of recording, which maintains the reference connection as
the EEG cap reference electrode. To use the default scheme,
simply place a jumper in the highlighted orange positions
depicted in gure2(b) (bottom), this will connect the Pak-50
reference position with the Omnetics reference position.
To re-route any of the reference connections to an external
electrode, place a jumper in the highlighted green positions
depicted in gure2(c) (bottom), and connect an external elec-
trode to the corresponding header pin (2c, bottom), this will
connect the Omnetics reference pin to an open header pin on
the breakout board. Additionally, the Omnetics connector on
the far right of the breakout board has up to four inputs that
can be re-routed from the EEG Pak-50 connector to header
pins allowing the recording of EMG and EOG, for example.
To re-route these inputs, place a jumper in the blue positions
depicted in gure2(c) (bottom) and an external electrode to
Figure 2. Open Ephys EEG breakout board. (a) Unpopulated board showing Pak-50 connector positions for connecting to electrode
cap (green), Omnetics positions for connecting to Amplier, headstage (blue), header pins for re-referencing, and sourcing additional
electrophysiological signals such as EMG or EOG (orange). (b) Default connection; (top gure) jumpers placed in the red positions route
four single EEG cap connections (Pak-50) to the Omnetics connector to record all EEG channels, jumpers placed in the orange positions
route EEG reference position to Omnetics connector; (bottom gure) the board layout corresponding to the default schematic shows
jumpers colored red and orange for correct placement for the positions detailed in the schematic. (c) Re-routing connections; (top gure)
jumpers placed in the blue positions re-route up to four connections from the Omnetics connector on the far right to allow recording
other biopotential measurements such as EMG or EOG, jumpers placed in the green positions re-route the reference connections for the
corresponding Omnetics connectors, allowing you to specify the reference of the signal; (bottom gure) the board layout corresponding to
the re-routing schematic shows jumpers colored in blue and green for correct placement for the positions detailed in the schematic, as well
as two connector pins of the same colors to show the corresponding connections for external electrodes (out of gure). For top guresin (b)
and (c) solid black lines indicate recorded sources, dotted black lines indicate non-recorded sources.
J. Neural Eng. 14 (20 17) 035002

C Black etal
5
the corresponding header pin (2c, bottom). Headers can be
purchased at any electronic store for under $3.00 in total.
Signals sent through the Open Ephys + EEG breakout
board are received and amplied by an Open Ephys compat-
ible Intan headstage (gure 1(c); one example shown here but
up to 4 could be used). The headstage is a small, thumb-sized
(2.2 × 1.3 cm) printed circuit board (PCB) that houses the
amplier chip used to amplify incoming EEG signals. Open
Ephys implements low-power Intan Technologies amplier
chips (RHD2000) that can amplify 32 or 64 channels of neural
data by 192 V/V with an output range of ±5 mV. Analog sig-
nals are multiplexed, which allows a large number of channels
to share a single 16-bit ADC. These chips have low input-
referred noise (2.4 µVRMS upper bound) and large range upper
(100 Hz–20 kHz) and lower (0.1 Hz–500 Hz) bandwidth set-
tings, making them ideal for a variety of neural recordings.
Some EEG systems use DC ampliers, which allow for
recording signals with little to no frequency content. While
the Intan chip is not a DC amplier, the bandwidth settings
can be adjusted using off-chip resistors to be 10 Hz–30 kHz,
and 0.02 Hz–1.0 kHz for upper and lower bandwidths, respec-
tively (Technologies I 2013). This allows for recording slower
oscillatory events such as infraslow oscillations (Vanhatalo
etal 2004). The headstage has two connective ports; one to
interface recording electrodes to amplier input (male 36
channel Omnetics connector), and the other (Omnetics PZN-
12-AA) to interface via serial-peripheral interface (SPI) with
the data acquisition board. From here an SPI cable transfers
data through to the data acquisition system (gure 1(d)).
Compatible Open Ephys headstages can be purchased directly
through Intan Technologies (table 1) along with SPI cables.
The Open Ephys acquisition board (gure 1(d)) allows for
recording between 32 and 256 channels with USB 2.0, or up
to 512 channels using USB 3.0. While such a high recording
count is excessive for most EEG experiments, which typi-
cally record between 32–128 channels, the ability to record
high channel counts comes at no extra cost to the user as
their recording capacity is dictated only by the number of
headstages and SPI cables they purchase. This means that
to move from a 32-channel system to a 128 channel system,
the user will need to purchase three more headstages and
SPI cables. Amplier outputs are sent to the headstage con-
nectors (gure 3(e)) of the acquisition board. The heart of
the acquisition board is an Opal Kelly XEM6010 eld pro-
grammable gate array (FPGA), which receives input from
peripheral devices (e.g. the EEG cap) to process and sort
incoming data. The data is then sent via USB or PCI express
serial bus to transfer data from the acquisition board to the
PC. Additionally, there are 8 analog input/output (IO) ports
(gures 3(a) and (b)) to register event data sent from external
devices, and 8 digital IO ports (gures 3(c) and (d)) for reg-
istering analog signals. The Open Ephys acquisition system
can be purchased through the Open Ephys webstore (www.
open-ephys.org/store/).
Finally, the PC (gure 1(e)) accepts incoming data packaged
from the acquisition board. Here, streaming data can be visual-
ized through the Open Ephys graphic user interface (GUI). The
Open Ephys GUI can be downloaded for free as an executable
(www.open-ephys.org/gui/) or can be compiled from source
code (https://github.com/open-ephys/GUI). The Open Ephys
GUI is multi-platform, working on Linux, Windows, and Mac
OS. Essentially, any computer would be compatible for run-
ning the Open Ephys GUI and recording data, so long as it has
a USB interface. Depending on the intentions of use a powerful
computer might be required to also run data analysis. However,
for the purposes of running the Open Ephys GUI and recording
data, any standard laptop or desktop will work.
In total, with current pricing, a brand new Open
Ephys + EEG system, including a new EEG cap and comp-
uter, will cost approximately $7060.97 on the low-end, which
consists of a 32 channel system with a passive cap, and
$25 247.10 on the high end, which consist of a 128 channel
system with an active cap. However, if the user can utilize
a previously purchased EEG cap and computer, the total
cost of the system will be between only $5130.97 (32-chan-
nels)–$9207.10 (128-channels).
1.2. Open Ephys + EEG assembly and timeline
Open Ephys + EEG requires minimal work once the proper
components have been purchased (table 1). At this time, the
Open Ephys + EEG breakout board schematics and design les
as shown can be freely downloaded (www.github.com/open-
ephys) and sent to a company for manufacturing. Many com-
panies offer PCB manufacturing. For breakout board shown
in gures1(b) and 2, we employed seeed studio (www.seeed-
studio.com) for printing. In order to submit a print request, you
upload the design les to the vendor website, and then select
specics about the design that will be necessary for printing. For
seeed studio, the only necessary options are a PCB dimension
of 10 cm × 10 cm, and two printing layers. The other options,
such as PCB quantity, PCB color, surface nish, etc, can be set
to their default values or changed for preference.
The PCB will come separate from the Omnetics connec-
tors, Pak-50 connectors, and the headers; however, setting
Figure 3. Input and output connections of Open Ephys Acquisition
Board. (a) ±5 V analog output, (b) ±5 V analog input, (c) 0/5
V digital output, (d) 0/5 V digital input, (e) SPI terminal for
connecting to Intan based headstages. Photo adapted from www.
open-ephys.org.
J. Neural Eng. 14 (20 17) 035002

C Black etal
6
up the complete EEG breakout board requires minimal self-
assembly consisting of soldering together a few key comp-
onents (gure 2, table1). Assembly is quite straightforward
and listed in 3 easy steps here:
1. Solder one Pak-50 connector to each of the four positions in
gure2(a) (green). The Pak-50 connectors are through-
hole mounted, and contain a mixture of mounting pins,
signal pins, and a ground and reference pin.
2. Solder one Omnetics connector to each of the four posi-
tions in gure 2(a) (blue). The Omentics connectors
are surface-mount devices (SMDs), which means the
solder connections all occur on the surface of the PCB,
as opposed to a through hole mount, where soldering
connections occur on the opposite side of the PCB.
Soldering SMDs can be difcult, especially when dealing
with such small connectors. One method to solder these
pieces is to carefully deposit a small amount of solder to
the surface pads of the connector on the PCB. Then, line
up the Omentics connector so all the feet are on top of
the corresponding pad; you may want to hold the con-
nector in position using a small clamp or carefully using
your hands. Once secure, place the soldering iron onto
the connector feet, individually, and press them into the
deposited solder. After soldering the Omnetics connec-
tors, apply epoxy around the base of the connector where
it meets the PCB. This will ensure stability of the connec-
tors, as the connections formed by the solder will break
under too much mechanical stress.
3. Solder the header pins into each of the positions shown in
gure2(a) (orange).
Timeline: Altogether this process should take anywhere
from 2 to 4 h, the bulk of this time being the soldering of the
Omnetics connectors as the contacts are quite small. Once the
epoxy has set, the adaptor will be ready for use.
1.3. Electrical safety
Electrical safety is a source of concern when dealing with
EEG as the electrodes provide a low-resistance path for
electrical current to ow. If there are any faults in wiring
or a power surge occurs, it is possible that current will ow
through the electrodes, to the person, and out through the
ground. Inducing currents into a person can lead to excitation,
heating, and burning of tissue, which can result in pain, injury,
or in extreme cases, death. At high enough currents (0.1–100
A), a few seconds of exposure to the mains voltage is enough
to produce these effects (Webster 2009). A solution for this
problem is to electrically decouple the subject from the mains
supply (Ebner etal 1999).
One method of isolation, which is currently with Open
Ephys + EEG, is implementing a battery pack. This is the pre-
ferred method of most commercially available systems. The
battery pack is intended to run the entire system from a power
source that is electrically isolated from the mains voltage. A
downside to this method is the fact that the length of recording
will be based off of the load imposed on your battery pack and
its internal powering capacity. Another method is to create a
power isolation circuit that decouples the subject completely
from the isolation power (Tyner et al 1983, Tallgren 2006).
This is currently being developed for Open Ephys (https://
github.com/open-ephys/headstage-isolation-board) in accord-
ance with IEC 60601-1 standards for medical electrical equip-
ment. The isolation circuit will create an interface between
the ampliers and the acquisition board. Data from the ampli-
ers will be transferred via Analog Devices’ digital isolators
(ADuM240 series), which have previously been implemented
for meeting above IEC standards, power will be provided to
the amplier by a DC-DC transformer, while ground planes
will be separated into an isolated participant ground (ampli-
er side) and the true ground (acquisition board side) (Abtahi
etal 2015). These methods serve to electrically decouple the
amplier from the mains supply so that potentially dangerous
currents cannot reach the participant (Piipponen et al 2007,
Lee etal 2012). This isolation circuit, however, is currently
still in development. We advise those interested in using the
Open Ephys + EEG system for human recordings consult
their Institutional Review Board (IRB), and determine the
appropriate safety standards and practices as they apply to
their institution.
2. Using the Open Ephys GUI
Once all components are purchased and assembled, you will
only need to congure the Open Ephys GUI to begin recording.
After downloading the GUI from (www.open-ephys.org/gui/),
start by clicking on the Open Ephys executable, which will
open a new window (gure 4). On the left hand side of the
window you will see a list of Processors (gure 4(a)) con-
sisting of Filters, Sinks, and Utilities. For the purposes here we
will only describe the minimal setup you will need for running
a basic EEG recording; however, many of the processors in the
GUI may be useful for more advanced EEG experiments. The
GUI works by dragging and dropping modules into the signal
chain (gure 4(b)), which sets the path for incoming data.
First, drag and drop the ‘Rhythm FPGA’ module to the signal
chain (gure 4(c)). The ‘Rhythm FPGA’ module is what initi-
ates communication with the FPGA in the acquisition board.
Here you have control over the acquisition board; you can
change the sampling rate, toggle the recording of all digital
and analog channels, control processes of the ampliers, and
even change the operation of LEDs on the board. Next, it will
likely be useful to incorporate a lter; as most EEG signals
are recording within the 0.1–100 Hz range. Drag and drop the
‘Bandpass Filter’ module to the right of the ‘Rhythm FPGA’
module in the signal chain (gure 4(d)), and set the lowcut to
0.1 and the high cut to 100, for example. This will then take
the incoming raw data from the FPGA and lter it within the
prescribed band. Finally, to visualize the data in real-time, drag
and drop the ‘LFP Viewer’ module to the right of the ‘Bandpass
Filter’ module in the signal chain (gure 4(e)). If you click on
the top hat on the ‘LFP Viewer’ in the signal chain, it will open
a new viewing window where you can look at incoming data,
toggle the view, and change the scaling.
To save the data, simply click on the drop down arrow on
the top right of the Open Ephys GUI window. In the prompt,
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C Black etal
7
select the desired le format (discussed in section 3.1), le
path, and le name to save (gure 4(f )).
3. Data analysis
Streamlining and simplifying data processing is an important
hallmark that new recording methods must have in order to
be desirable for consumer use. In an attempt to make the pro-
cessing of Open Ephys data an easy transition for EEG users,
here we describe how Open Ephys data can be handled a
variety of ways. Our exible open-source methods allow indi-
vidual users the freedom to create their own analysis tools for
their system and needs. Below we highlight two main consid-
erations in handling Open Ephys + EEG data: (1) data types
and conversion (section 3.1), and (2) noise reduction (section
3.2).
3.1. Data types and conversions
Open Ephys data can be saved directly to four le types: Open
Ephys, Flat binary, NWB, and KWIK. Switching which data
format you save to is simply done through the Open Ephys
GUI by selecting one of the previously listed formats from
a drop down box at the above toolbar. For the purpose of
description below, we chose the Matlab compatible Open
Ephys format and describe how to convert to a Matlab read-
able le. The Open Ephys les types are ‘.continuous’, which
saves continuous electrophysiology data, ‘.events’, which
saves digital events, and ‘.spikes’, which saves spike sorting
events. Each data type can be converted to Matlab, but for EEG
purposes we focus on the ‘.continuous’ data type, as it will
provide us the EEG traces for each recorded channel. In order
to convert ‘.continuous’ les to use in Matlab, an Open Ephys
analysis toolbox, available for free online (https://github.com/
open-ephys/analysis-tools), must be used. Once downloaded,
Matlab can be used to easily convert the ‘.continuous’ data
type to the ‘.mat’ format, as shown by the code below:
Figure 4. Open Ephys GUI; example recording setup. (a) List of processors that can be dragged and dropped into the (b) signal chain.
(c)The Rhythm FPGA initializes the Acquisition Board and opens up communication for data to be sent along the signal chain.
(d) A bandpass lter lters the data within any prescribed range. (e) The LFP Viewer allows for visualization of incoming data. (f) Data is
easily saved by clicking on the drop down button and entering in the relevant information.
Figure 5. Schematic of standard 10–20 electrode cap layout.
Recording electrodes Pz and C3 (light green) used for analysis in
sections4.1 and 4.2, reference electrode (light blue), and ground
electrode (light grey).
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The function ‘load_open_ephys_data’, from the Open
Ephys analysis toolbox, essentially parses and translates
the custom ‘.continuous’ le type. The input of the func-
tion is the lename in a string, and it will return double-
precision oating-point values of the data for that channel,
stored in micro volts, and the timestamps, stored in ms.
Information regarding events, such as timing and the digital
channel that the event occurred on during the recording, is
also obtained. This is stored in the form of a Matlab struc-
ture that contains information such as the data format used,
the sampling rate of the recording, the channel number, the
buffer size, and the event channels along with their corre-
sponding timestamps.
This conversion is useful when analysing data with custom
Matlab scripts. However, in the data analysis described in
the next section, we have also used the open-source Matlab
toolbox EEGLAB (Delorme and Makeig 2004), available for
download at http://sccn.ucsd.edu/eeglab/. In order to import
data into EEGLAB, it is necessary to rst concatenate all of
the data into a single matrix:
% Converting 32 channels of data from '.continuous' to '.mat'
for ch = 1:32
[data, timestamps, info] =
load_open_ephys_data(['100_CH',num2str(ch),'.continuous']);
save(['ch',num2str(ch),'.mat'],'data','timestamps', 'info');
end
Loading data into EEGLAB then requires knowledge of
the sampling rate and the number of channels loaded. While
this is one example, the exibility of Open Ephys data ena-
bles use for any number of analysis software or toolboxes.
There are many other open-source software packages that can
be used to analyse EEG data, see also MNE at http://martinos.
org/mne/stable/index.html, but for simplicity we have focused
on EEGLAB.
3.2. Noise reduction
Environmental noise is always an issue with electrical
recording methods, and human EEG is no exception. Variations
or errors in improper grounding, capacitive coupling of par-
ticipants to the mains supply, and AC devices all contribute to
overall noise registered in human EEG (Ferree etal 2001). As
the Open Ephys + EEG system is not immune to noise, here,
we describe observed noise and means of reduction applied to
the data collection in section4.
Many electrophysiological recordings, including EEG,
contain what is referred to as line noise, which is noise gen-
erated from the power lines and depending on the country,
is either 50 or 60 Hz. While line noise is outside of the fre-
quency bands of interest for many EEG applications, it is
best to reduce line noise as much as possible (Ferree et al
2001). Electronically, noise can be reduced by appropriate
implementation of recording ampliers. Intan amplier chips
make use of unipolar differential recording, meaning that the
incoming signal from each electrode is compared against a
common reference. This common-reference has a much lower
impedance than the input impedance for each signal electrode
(Technologies I 2013), and therefore produces a large noise
signal (Winter and Webster 1983). By tying the true common-
reference, i.e. the ground, to the current reference, the noise
in the signal path was compared to the ground signal, which
was also contaminated with the same noise (Light etal 2010),
and hence removed. Additionally, impedance measurements
were taken throughout EEG application to assure there was
no impedance mismatch across electrode channels, as this can
introduce noise.
Ground loops can also provide a source of noise to the
recording system. Electrical devices generate a leak current to
the ground that causes small differences between the ground
at different points (Tyner et al 1983). Removing any devices
connected to ground will drive down leakage currents that
produce noise within the ground.
Finally, all of our recordings were conducted inside a shielded
room. A shielded room serves as a measure to absorb external
electromagnetic interference, prohibiting our system from
picking up the noise outside the room. As with all EEG systems,
even with proper referencing, grounding, and shielding, 60 Hz
noise from the mains supply may still persist. This is because
capacitive coupling between the participant and any nearby power
lines still exists (Ebner etal 1999). Therefore, raw data traces will
still exhibit a 60 Hz interference (i.e. line noise), albeit reduced
in magnitude. To remedy this, it is standard practice is to apply a
digital 60 Hz notch lter after data acquisition (Ferree etal 2001),
to suppress the remaining 60 Hz noise can be suppressed.
% Concatenating data into a single '.mat' le for use with EEGLAB
all_channels = [];
for ch = 1:32
load(['ch',num2str(ch),'.mat'], ‘data’);
all_channels(:,ch) = data;
end
save(‘all_eeg.mat’, ‘all_channels’);
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4. Examples
In this section, we present three examples of commonly
recorded EEG signals (low frequency rhythms, sensory
evoked responses, and EMG activity) obtained with the Open
Ephys + EEG system. EEG was recorded with a commer-
cially available electrode cap from Brainvision, using the stan-
dard 10–20 system (gure 5). In each example, data analysis
was conducted with Matlab using either custom scripts or the
open source Matlab toolbox, EEGLab (Delorme and Makeig
2004). Raw data was down sampled to 250 S/s, to reduce com-
putational burden, and then visually inspected to remove any
large magnitude noise. Data was then band-pass ltered from
0.1 to 100 Hz and notch ltered at 60 Hz to remove line noise.
Plotted ERP’s received an additional low-pass 40 Hz lter for
the sole purpose of visualization of lower frequency content
of the ERP, such as the P300 (Ai and Ro 2014). Depending
on the data in question, one may not desire to use a 40 Hz
low-pass lter, in the event that there is meaningful activity at
higher frequencies. After ltering, we removed uncharacter-
istic large amplitude (>100 µV) signals. All EEG data under-
went independent component analysis (ICA)-based artifact
rejection using the FastICA algorithm (Vigário et al 2000).
As each EEG electrode recording is the mixed signal of a sum
of neural sources, ICA separates the mixed signals into their
individual components (sources) along with their respective
weights. The components attributed to muscle, cardiac, and
eye blink artifacts were visually identied and were weighted
Figure 6. Occipital alpha band activity from three different subjects, (a)–(c) taken from the Pz electrode during an alert (eyes open) and a
rest (eyes closed) state. The top left plot for (a)–(c) are the raw, unltered voltage traces of the transition state for eyes closed to eyes open
(denoted by the dotted black line). The bottom left plot for (a)–(c) are the ltered voltage traces (0.1–100 Hz band-pass, 60 Hz notch) that
show a slight reduction in power, but still strong presence of alpha band activity. Finally, the right plot for (a)–(c) show the power spectral
density plots of eyes open (dotted grey line) and eyes closed (solid black line) states. There is a prevalent alpha peak (around 10 Hz) for
each individual in the eyes closed state, and a strong reduction in alpha power in the eyes open state.
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to zero. More information about the FastICA algorithm, along
with a free downloadable MATLAB software package can be
found at Aalto University’s Deparment of Computer Science
(https://research.ics.aalto./ica/fastica/).
4.1. Example of stable spontaneous low frequency rhythms:
eyes-closed alpha (7–14 Hz)
Monitoring changes in EEG from a relaxed eyes-closed state
to a focused eyes-open state generated robust changes in alpha
band (7–14 Hz) activity over occipital cortex (gure 6), con-
sistent with many prior EEG studies (Berger 1935). The data
shown in gure5 was collected while participants sat comfort-
ably in a chair and stared at a xation point. An auditory cue
was given every 10 s instructing the participant to either open,
or close their eyes. During this time, EEG was recorded using
the EEG Breakout Board and a standard, commercially available
electrode cap from Brainvision. Raw (gure 6(a)) and ltered
(gure 6(b)) traces from electrode Pz (gure 5) showed increased
magnitude in oscillatory alpha band activity during eyes closed
epochs as compared to eyes open epochs. Additionally, power
spectral density (PSD) calculation of one epoch (10 s) of eyes
closed (gure 6(c), solid black traces) and eyes open (gure 6(c),
dotted grey traces) from the Pz electrode shows an increase in
power in the alpha band for the eyes closed state.
We compared recordings from the Open Ephys + EEG
system to a standard Brainvision actiCHamp EEG system,
during the eyes-open eyes-closed task described above using
the same Brainvision actiCap (gure 7). First, the partici-
pant, whose data is shown in gure7, performed the task with
the Open Ephys + EEG system and was then switched to
theBrainvision actiCHamp system. During the entirety of the
recordings, the EEG cap was not removed and the participant
remained seated in the same room. Both data sets were ana-
lyzed as data from gure6; they were downsampled to 250 S/s,
bandpass ltered from 0.1 to 100 Hz and notch ltered at 60 Hz
to remove the residual line noise. Following this, ICA was per-
formed to remove eye blinks from each data set. Representative
traces from the transition of an eyes closed to an eyes open state
is shown in gure7(a) for both systems (top gure: Brainvision,
bottom gure: Open Ephys + EEG). Additionally, a PSD was
calculated over the 2–8 s (6 s) mark for each eyes closed epoch
of both systems. The PSDs for each epoch were averaged for
each system (gure 7(b)). Qualitatively, both average PSDs
provide similar results, with a peak at 11 Hz for this subject.
The only difference between the two systems is the low fre-
quency region comp onent of the pink, or 1/f, noise at 0–1 Hz.
This was anticipated since the Open Ephys + EEG system
uses an AC amplier, while the Brainvision system uses a DC
amplier (see section 1.1). Quantitatively, the average alpha
band power for the Brainvision system was 14.84 ± 1.44 (µV2
Hz−1) and the average power for the Open Ephys + EEG
system was 14.93 ± 0.94 (µV2 Hz−1). We also calculated the
signal-to-noise (SNR) ratio of each system by dening the
signal to be power in the 8–14 Hz band during eyes closed, and
the noise to be power in the 8–14 Hz band during eyes open.
This gave an SNR of 12.7 dB for the Brainvision system, and
13.6 dB for the Open Ephys + EEG system.
4.2. Example of sensory evoked potentials: tactile detection
Sensory evoked potentials (SEPs) shown in gure8 were gener-
ated by delivering brief taps to a subject’s nger-tip during a tac-
tile detection task, as described in prior studies (Jones etal 2007).
More specically, volunteers placed the third digit of their right
hand over a custom made tactile stimulator, which delivered brief
taps to the ngertip via a plastic screw that was driven by a piezo-
electric bender. Stimuli were given as 100 Hz, 10 ms sine waves
of varying amplitude dynamically titrated to maintain stimulus
strength at perceptual threshold (50% detection). During this
Figure 7. Signal comparison between Brainvision actiCHamp system and Open Ephys + EEG system using the same Brainvision actiCap
electrode cap in recording eyes closed alpha activity. (a) Transition from eyes closed to eyes open in the (top gure) Brainvision system and
the (bottom gure) Open Ephys + EEG are very similar. (b) Average PSD of Brainvision (dotted black line) and Open Ephys + EEG (solid
blue line) for eyes closed epochs.
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11
time, 32 channels of EEG were recorded using the EEG Breakout
Board and a BrainVision electrode cap.
Figure 8 shows grand averaged SEP data from suprath-
reshold (i.e. detected) tactile stimuli from electrode C3 (gure
5), averaged over 3 subjects (n = 78 stimuli per subject). SEPs
from perceived stimuli produced generic waveforms, con-
sistent with previously reported EEG tactile evoked responses
(Jones etal 2007, Ai and Ro 2014). A large positive deec-
tion begins at ~330–500 ms post-stimulus; a two-tailed t-test
(p < 0.05) was performed to highlight the region of signi-
cance of the ‘P300’ from zero (green box gure8). The RMS
noise, taken to be the pre-stimulus time period (−200–0 ms),
was calculated to be 6.0117 ± 2.069 µVrms.
4.3. Example use of EEG breakout board: EMG recordings
The EEG breakout board also allows recording of additional
electrophysiology signals (see gure 2(c) for example con-
nectivity). Here, we show an example recording EMG data
(gure 9). EMG electrodes were placed on the subject’s right
forearm, over the exors, with a reference electrode placed
on the wrist. Recordings were carried out with the Open
Ephys EEG Breakout Board, using the method described in
section1.1, and disposable EMG electrodes. EMG data was
monitored during periods of rest and periods of exion move-
ment of the hand and ngers. Periods of rest were marked by
stagnant, low amplitude activity, while movement was marked
by large amplitude (>0.3 mV) activity (gure 9).
5. Discussion
We have described the parts and methods necessary to
assemble a new low-cost, exible and open-source human
EEG system using the Open Ephys platform. We have also
provided details of how to analyze data from this system and
evidence that this system can produce reliable recordings of
the most commonly measured EEG signals; low frequency
rhythms and sensory evoked responses, as well as EMGs.
Figure 8. Grand average sensory evoked potential at electrode C3 (solid black) of perceived stimuli from tactile detection task. Stimulus
delivery occurs at 0 msec. Standard deviation for average of three subjects (dotted black), 78 trials each. P300 deection shown at ~400 ms
in light green (p < 0.05). A 40 Hz low-pass lter was used for visualization.
Figure 9. Example of acquired EMG data from forearm exion using Open Ephys with the EEG adaptor (gure 2). EMG electrodes were
placed on the subject’s forearm with a reference on the elbow; the forearm was at rest for the rst 2 s, and then the forearm was exed from
2 to 3 s, before resting once more.
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Additionally, we have shown recording efcacy in the Open
Ephys + EEG system by running a side-by-side comparison
with a commercial EEG system. Building on Open Ephys
technology, this system can help transform the applicability
of EEG to a wider user base by providing a stable and easily
assembled system at an affordable price.
5.1. Expanding the utility of Open Ephys + EEG
Due to the modular style of the Open Ephys + EEG system,
the research applications are almost endless. One prom-
ising use of EEG is for closed-loop technologies; technolo-
gies that receive feedback from an EEG signal to drive an
external device, such as a computer cursor, a wheelchair, a
robotic arm, or an electric stimulator. In order for closed-
loop technologies to work properly, they have two basic
requirements; control and speed. With Open Ephys + EEG,
signals such as ERPs or neural oscillations can be tied to
a digital output on the acquisition board to allow for inter-
facing with an external device. For example, transcranial
alternating current stimulation (tACS) has been used to
selectively modulating working memory (Jausovec and
Jausovec 2014). Our system could enable a closed-loop
design to selectively apply tACS during working memory
by, for example, recording an EEG biomarker of working
memory, such as the power of theta oscillations (Jausovec
and Jausovec 2014), and sending a digital signal or commu-
nicating over USB to an external tACS device to stimulate
only during theta activity. Signals from the tACS device can
also be sent to the Open Ephys + EEG system to allow for
syncing of external tACS events, such as the precise time of
initiation of a tACS pulse. A demonstration of closed-loop
capabilities with the Open Ephys system has already been
published in the context of triggering optogenetic stimula-
tion in mice (Siegle and Wilson 2014). Here, the Open Ephys
system was used in a closed-loop manner to trigger opto-
genetic stimulation by detecting peak theta power through
the Open Ephys GUI and in turn driving a pulse generator
to activate an LED. This setup boasted a ~20 ms loop time
for signal detection and subsequent optogenetic stimulation
(Siegle and Wilson 2014). Additionally, the Intan amplier
chips used in this system have a ‘fast settle’ function that
blanks the electric history of the recording ampliers within
a few hundred microseconds (Technologies I 2013). This
is highly applicable in closed-loop devices since quickly
turning on and off the ampliers can eliminate high voltage
transients stemming from environmental noise, accompa-
nying devices, or direct electrical excitation of electrodes,
enabling immediate, reliable neural recordings.
The use of EEG as a clinical tool is invaluable. EEG is used
not only as a diagnostic measure (e.g. monitoring epileptic
activity) but it is also utilized as a therapeutic tool (e.g. man-
aging ADHD with neurofeedback). Unfortunately, the high-
cost of commercially available EEG systems, as well as the
technical bar for self-use, makes it impractical for individuals
to have EEG in their home. However, the low price and high
customizability of Open Ephys + EEG advances the possible
application to affordable home use. As such, patients would
not need to commute to a hospital for a neurological checkup,
and near-future therapeutics could easily be setup at a per-
son’s bedside.
5.2. Current limitations and solutions
Even though there are proven benets to open-source tech-
nology and information sharing, there are also some limi-
tations. They do not provide staffed customer support for
open-source projects, service technicians who can come look
at your device if something is wrong, or paid engineers to
bring you new tools and products. With open-source tech-
nologies, the burdens of development, ingenuity, and trou-
bleshooting all fall upon the user. This can be a daunting
proposition, especially for those who are inexperienced when
it comes to electronics or coding. Therefore, open-source
technologies require a strong user base to thrive. The users
are what provide stability to the project and assurance to those
who are seeking help when developing new ideas and tools
within an open-source project space. That being said there are
many successful open-source projects in the eld of human
electrophysiology complete with a large user base that openly
shares information, reduced production cost of technology,
and a wide range of tools for generating new experimental
techniques and testing novel hypothesis; and Open Ephys
strives to these ideals. Currently, Open Ephys is being used
in over 70 labs throughout the world. The community shares
a variety of tools, both hardware and software, across multiple
platforms; provides comprehensive user documentation on a
dedicated wiki, and design documents and code on github;
and posts discussions on a google groups based forum. There
is even have a small webstore where hardware tools can be
purchased. The Open Ephys system is heavily designed with
expansion in mind, and comes ready to use with other open-
source products such as RTXI, a software interface for real-
time data acquisition and control, and Pulse Pal, a hardware
precision tool for controlling stimuli (Lin etal 2010, Sanders
and Kepecs 2014, Siegle etal 2015).
While the system described has proven to be efcacious in
recording common human neural signals, there are some lim-
itations. First, our system is completely tethered; meaning,
the electrode cap attached to the patient is connected directly
to the acquisition board and the computer. This makes the
use for the system impractical in recording EEG during
more complex behaviors involving movement, or longer-
term recordings. To address this issue, our system could be
easily adapted to wireless recording, which would provide an
ideal method of data transfer in running untethered experi-
ments. Schematics and documentation are provided for all
hardware and software of this system, therefore creating a
wireless connection for EEG use on Open Ephys would be
simple to develop, troubleshoot, and implement. Fortunately,
the data requirements necessary for EEG recordings are rela-
tively low if you keep at a reasonable sampling rate. Since the
Intan amplier chips have 16-bit ADC resolution, recording
32 channels of EEG at 1 kS s−1 requires a data transfer rate
of 512 kbps. While such a data rate could be handled with
a wi- or even a Bluetooth link, adding more channels or
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C Black etal
13
making wireless connectivity compatible for different modes
of recordings will inevitably lead to larger data rates that
might be difcult to maintain.
Second, while our Open Ephys + EEG design (gure
1) fosters the ability to freely connect with an Open Ephys
compatible headstage allowing versatility to move between
recording from humans to animals, it also leaves the head-
stages, and therefore the ampliers, exposed with little pro-
tection and therefore subject to break. One way around this
would be to place the Intan amplier chips directly onto the
EEG breakout board. This would get rid of the need for a head-
stage, increase the stability of the ampliers, and reduce the
chances of damage. Such a modication would be straight-
forward and could be done easily in-house, and if the number
of Open Ephys + EEG users increases it could become com-
mercially available.
Third, our system currently only supports certain commer-
cially available electrode caps due to the fact that it uses spe-
cic connector types that are not universally used by all EEG
electrode caps. However, our methods can be easily expanded
to use other electrode caps. Developing a custom EEG adaptor
for a cap type that is not currently supported only requires
knowledge of the electrode cap pinout, i.e. what electrodes
correspond to what pin in the connector. Once that informa-
tion is obtained, a PCB can be fabricated that receives signals
from the electrode cap and sends them to necessary connec-
tors for the Open Ephys system.
Acknowledgments
We would like to thank Open Ephys community for not only
developing high-quality, open-source tools, but also for fos-
tering discussion and aiding in trouble-shooting. We would
also like to thank Reid Harrison and Intan technologies.
Work and writing were supported by the National Institutes
of Mental Health (R01MH106174), the Brown Institute for
Brain Sciences and the Norman Prince Neurosciences Insti-
tute. This material is based upon work supported in part by
the Department of Veterans Affairs, Veterans Health Admin-
istration, Ofce of Research and Development, Rehabilita-
tion Research and Development Service, Project N9228-C.
The views expressed in this article are those of the authors
and do not necessarily reect the position or policy of
the Department of Veterans Affairs or the United States
government.
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