Open Ephys Acquisition Board

Flexible and affordable multichannel electrophysiology. The board is part of a complete platform for multichannel electrophysiology experiments. You can build it at low cost and modify it to enable new types of experiments.

Key Features

• Plug and play (Mac & Win & Linux)

• Industry standard Intan amplifier & A/D chips

• 1–30 kHz Sampling Rate

• Low Noise 2.4 µV rms

• Large Input Range ± 5 mV

• True real-time audio monitoring

• 8 indicator LEDs

Connectivity

• Up to 8 headstages on 4 tethers (or up to 4 128-channel headstages)

• Standardized digital headstage cables

• USB 2.0 or 3.0 data transmission

• 8 TTL inputs 8 TTL outputs

• 8 analog inputs (±5V or 0–5V)

• 8 analog outputs (±5V)

• Standardized I/O cables

Standardized I/O connectivity

The Acquisition board has 8 channels each of digital in/out, 8 channels of analog input  (±5V or 0-5V so these can record from microphones or piezo sensors) and 8 channels of ±5V analog output. All this I/O uses the same standardized HDMI connectors (wiki), so you can use the same breakout-boards for everything, or even incorporate a HDMI connector into your equipment and connect the system directly without any bulky BNC cables.

The same connector standard is also shared by our Arduino I/O module (github) that can be used to generate or read digital signals on any PC.

Removable FPGA module

The acquisition board uses a standardized FPGA module by Opal Kelly. This means that the same acquisition board can be used with USB 2.0 or USB 3.0 interfaces simply by swapping the FPGA module. You can also make use of the FPGA to build very low latency closed-loop experiments. See our wiki for a list of possible applications of this technology.

Easy to use closed-loop feedback

Neural data is streamed to the PC with a delay of ~10 ms. For many applications, this is fast enough to detect a neural or behavioural state and intervene with stimulus changes, optogenetic manipulation or whatever your experiments demand.

Through the Open Ephys GUI, closed-loop stimulation algorithms can be written in C++, or Python and Julia using special versions of the software that are currently under development.