Dry - Mobile - Fast - EEG

Wearable Sensing’s wireless DSI-VR300 is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-VR300 takes on average less than 1 minute to set up, making it the ideal solution for scientists in need of a simple, easy to use, EEG system. Our patented sensor technology not only delivers uncompromised signal quality but also enables our system to be virtually immune against motion and electrical artifacts. As a result, the DSI-VR300 can be utilized in virtual or augmented reality, while also allowing researchers to take their experiments out of the lab, and into the real world. 

The VR300 has sensor locations in the back of the head, and therefore optimized for visually evoked potentials like P300 and SSVEP. The VR300 can be used with or without a VR headset

Used around the world by leaders in Research, & Brain-Computer Interfaces

Research-Grade Signal Quality

With over 90% correlation to research-grade wet EEG systems, the dry sensor interface (DSI) offers unparalleled quality and performance

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Extreme Comfort

Multiple adjustment points and a foam pad lined interior enable the system to be worn for up to 8 hours on any head shape or size

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Free Data Acquisition Software

All DSI systems include free, unlimited licenses of DSI-Streamer, our data acquisition software which can record raw data, in .csv and .edf file formats

Artifact Immunity

Faraday cage's, spring-loaded electrodes, and our patented common-mode follower technology, provides near immunity against electrical and motion artifacts

1 Minute Cleaning

Using 70% isopropyl alcohol and a cleaning brush, the DSI-VR300 only takes a minute to clean, 3 minutes to dry, and can be up and running on the next subject in minutes

Free API

All DSI systems include our free C based .dll API, which enables users to pull the raw data directly from the headset, for custom software on Windows, Mac OS, Linux, and ARM

Rapid Setup

The DSI-VR300 was designed for ultra-rapid setup, taking on average less than 1 minute to don, and works on any type of hair, including long hair, thick hair, afros, and more

Ambulatory System

DSI headsets have active sensors, amplifiers, digitizers, batteries, onboard storage, and wireless transmission, making them complete, mobile, wearable EEG systems

Cognitive Gauges

DSI systems exclusively work with QStates, a machine learning algorithm for cognitive classification on states such as mental workload, engagement, and fatigue

Virtual Reality Compatible

Bring EEG into the virtual world, using our VR adaptor kit, compatible with the HTC Vive Pro and Oculus Quest 2

3D Accelerometer

Every DSI system has an integrated 3D accelerometer, which can be used for head motion tracking

Wireless Synchronization

Our Wireless Trigger Hub simplifies the synchronization of DSI headsets with other devices. It features: 

  • 8 trigger input and output channels (independent and parallel port)
  • 4 Analog inputs for TTL pulses or other analog triggers with BNC and Stereo connectors
  • 2 Switch inputs for push buttons or photodiodes
  • 1 Audio input on a mono connector
  • All channels have an adjustable trigger threshold
  • All inputs are thresholded and sent as digital triggers on independent outputs and a parallel output
  • Parallel-USB interface is available as an option g standard cables.

An additional benefit of the Trigger Hub design is that it allows synchronization across multiple data sources that are distributed across multiple systems, each of which running at its own clock rate. One such case commonly experienced in EEG experiments involves the synchronization of EEG and eye-tracking measurements, where the inevitable clock drift that arises between two systems during extended measurements creates difficulty in aligning data to events across the two systems.


Locations optimized for:

  • Visual Stimuli
  • P300 Experiments
  • Alertness
  • Strong Emotions and Phobias
  • Some Sensory Associations


Locations optimized for:

  • Visual Processing
  • Visually Evoked Potentials
  • Steady State Visually Evoked Potentials (SSVEP)

EEG Channels

DSI-VR300: FCz, Pz, P3, P4, PO7, PO8, Oz, Linked Ears
DSI-VRVEP: FCz, POz, PO3, PO4, O1, O2, Oz, Linked Ears

Reference / Ground 

Common Mode Follower / A1

Head Size Range

Adult Size: 52cm – 62cm circumference

Sampling Rate

300 Hz (600Hz upgrade available)


0.003 – 150 Hz

A/D resolution

0.317 μV referred to input

Input Impedance (1Hz)

47 GΩ


> 120 dB

Amplifier / Digitizer

16 bits / 7 channels



Wireless Range

10 m


> 12 Hours


Data Acquisition

Real time, evoked potentials

Signal Quality Monitoring

Continuous impedance, Baseline offset, Noise (1-50 Hz)

Data Type

Raw and Filtered Data available

File Type

.CSV and .EDF

Data Output Streaming

TCP/IP socket, API (C Based), LSL

Compatible Software

Cognitive State Classification

Brain Computer Interface

SSVEP BCI Algorithms; BCI2000; OpenViBE; PsychoPy; BCILab

Data Integration / Analysis

CAPTIV; Lab Streaming Layer; NeuroPype; BrainStorm; NeuroVIS


Presentation; E-Prime

Real-Time P300 Drone Control

Dr. Soram Kim and his team at Handong Global University developed a drone control application using P300-based BCI, which can be used in both VR and AR. On average, the subjects’ performance was 90.88% (VR) and 88.53% (AR).



Chiossi, Francesco; Ou, Changkun; Mayer, Sven

Optimizing Visual Complexity for Physiologically-Adaptive VR Systems: Evaluating a Multimodal Dataset using EDA, ECG and EEG Features Conference


Abstract | Links

Klee, Daniel; Memmott, Tab; Oken, Barry

The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm Journal Article

In: Signals, vol. 5, no. 1, pp. 18–39, 2024.

Abstract | Links


Chiossi, Francesco; Turgut, Yagiz; Welsch, Robin; Mayer, Sven

Adapting Visual Complexity Based on Electrodermal Activity Improves Working Memory Performance in Virtual Reality Journal Article

In: Proc. ACM Hum.-Comput. Interact, vol. 7, 2023.

Abstract | Links


Klee, Daniel; Memmott, Tab; Smedemark-Margulies, Niklas; Celik, Basak; Erdogmus, Deniz; Oken, Barry S

Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration Journal Article

In: Frontiers in Human Neuroscience, vol. 16, 2022.

Abstract | Links


McLaughlin, Deirdre; Klee, Daniel; Memmott, Tab; Peters, Betts; Wiedrick, Jack; Fried-Oken, Melanie; Oken, Barry

Methodology and feasibility of neurofeedback to improve visual attention to letters in mild Alzheimer's disease Journal Article

In: Human-Computer Interaction, 2021.

Abstract | Links

Kim, Soram; Lee, Seungyun; Kang, Hyunsuk; Kim, Sion; Ahn, Minkyu

P300 Brain--Computer Interface-Based Drone Control in Virtual and Augmented Reality Journal Article

In: Sensors, vol. 21, no. 17, pp. 5765, 2021.

Abstract | Links


Pereira, Arnaldo; Padden, Dereck; Jantz, Jay; Lin, Kate; Alcaide-Aguirre, Ramses

Cross-Subject EEG Event-Related Potential Classification for Brain-Computer Interfaces Using Residual Networks Journal Article

In: 2018.

Abstract | Links