Dry Sensor Interface


Dry - Mobile - Fast - EEG

Wearable Sensing’s wireless DSI-24 is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-24 takes on average less than 3 minutes 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-24 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 DSI-24 has sensors that provide full head coverage with 19 electrodes on the head, 2 earclip sensors, and also has 3 built-in auxiliary inputs for acquisition of up to 3 auxiliary sensors. It also has an 8-bit trigger input to synchronize with other devices such as Eye-Tracking, Motion (IMU), and more.

Used around the world by leaders in Research, Neurofeedback, Neuromarketing, Brain-Computer Interfaces, & Neuroergonomics.

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-24 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-24 was designed for ultra-rapid setup, taking on average less than 3 minutes 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

Full Head Coverage

The DSI-24 has 3 easy adjustment points, which allows for accurate sensor positioning on any head shape or size, with a positional of accuracy of 1.5 cm on average

Child Size System

The DSI-24 Child Size system can fit on head circumferences from 48-54 cm, or about age 3-12. Since the system is extremely comfortable, and quick to put on, it is the ideal solution for children with Autism or other hypersensitivities

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.

Auxiliary Sensors

The DSI-24 has 3 auxiliary inputs on the headset, which allows for automatic synchronization of Wearable Sensing’s auxiliary sensors to the EEG. The sensors available include ECG, EMG, EOG, GSR, RESP, & TEMP. The sensor data is collected and recorded in our data acquisition software, DSI-Streamer, where you can view the EEG and Aux sensors in real-time.


EEG Channels

Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, T7/T3, T8/T4, Pz, P3, P4, P7/T5, P8/T6, O1, O2, A1, A2

Reference / Ground 

Common Mode Follower / Fpz

Head Size Range

Adult Size: 52cm – 62cm circumference
Child Size: 48cm – 54cm 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 / 24 channels



Wireless Range

10 m


> 24 Hours, Hot-Swappable Batteries

Onboard Storage

~ 68 Hours (available option)


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


Applied Neuroscience NeuroGuide; Brainmaster Brain Avatar; EEGer


CAPTIV Neurolab


Presentation; E-Prime

P300 Speller

Betts Peters, Dr. Melanie Fried-Oken, and their team at Oregon Health & Science University have developed a P300 speller using the DSI-24, and have validated its functionality on subjects with Locked-In syndrome

BCI on Individuals with Autism

Dr. Murat Akcakaya and his team at University of Pittsburgh created a BCI that was able to reliably differentiate between distress and non-distress conditions in 21 individuals with ASD on a single trial basis during a game with deception.

Quality Assessment of DSI-24

Dr. Stefanie Blain-Moraes and her team at McGill University did a comparative study of the DSI-24 and other dry EEG systems to a Gold Standard system using functional connectivity to assess signal quality and spectral characteristics


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Halford, Jonathan J; Schalkoff, Robert J; Satterfield, Kevin E; Martz, Gabriel U; Kutluay, Ekrem; Waters, Chad G; Dean, Brian C

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Arakaki, Xianghong; Shoga, Michael; Li, Lianyang; Zouridakis, George; Rostami, Ramona; Goldweber, Robert; Harrington, Michael

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Investigation of engagement of viewers in movie trailers using electroencephalography Journal Article

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Li, Lianyang; Pagnotta, Mattia F; Arakaki, Xianghong; Tran, Thao; Strickland, David; Harrington, Michael; Zouridakis, George

Brain activation profiles in mTBI: Evidence from combined resting-state EEG and MEG activity Conference

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Hairston, David W; Whitaker, Keith W; Ries, Anthony J; Vettel, Jean M; Bradford, Cortney J; Kerick, Scott E; McDowell, Kaleb

Usability of four commercially-oriented EEG systems Journal Article

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Soussou, Walid; Rooksby, Michael; Forty, Charles; Weatherhead, James; Marshall, Sandra

EEG and eye-tracking based measures for enhanced training Conference

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Estepp, Justin R; Monnin, Jason W; Christensen, James C; Wilson, Glenn F

Evaluation of a Dry Electrode System for Electroencephalography: Applications for Psychophysiological Cognitive Workload Assessment Journal Article

In: vol. 54, no. 3, pp. 210–214, 2010.

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Antonenko, Pavlo; Paas, Fred; Grabner, Roland; Gog, Tamara Van

Using Electroencephalography to Measure Cognitive Load Journal Article

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Fielder, James

Electroencephalogram (EEG) Study of Learning Effects across Addition Problems Technical Report

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