Dry Sensor Interface


Dry - Mobile - Fast - EEG

Wearable Sensing’s wireless DSI-Flex is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-Flex takes on average less than 5 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. 

The DSI-Flex has dry sensors on flexible cables, enabling scientists to place the electrodes in varying configurations on the head. These flexible sensors are designed to be screwed into custom caps, so that scientists can order 1 DSI-Flex, and multiple caps, allowing for rapid application of multiple electrode configurations. Every sensor on the DSI-Flex can be customized as either ExG, GSR, TEMP, and REP. It also has a 4-bit trigger input to synchronize with other devices such as Eye-Tracking, Motion (IMU), and more.

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

Svg Vector Icons : http://www.onlinewebfonts.com/icon
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

Svg Vector Icons : http://www.onlinewebfonts.com/icon
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-Flex was designed for ultra-rapid setup, taking on average less than 5 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

Customizable Caps

The DSI-Flex has a soft mesh cap where custom sensor locations can be placed upon order. Multiple caps can be ordered with a single DSI-Flex, enabling truly customizable sensor locations, while maintaining ease of set up, and security on the head


The DSI Flex system can be easily customized to replace the default EEG sensors with other DSI auxiliary sensors, such as ECG, EMG, EOG, GSR, RESP, and TEMP.

3D Accelerometer

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

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-Flex can be customized so that an EEG sensor is replaced with a DSI auxiliary sensor. There are up to 7 locations on the DSI-Flex, enabling any configuration of the following sensors: EEG, 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

Up to 7 Custom Sensor Locations

Reference / Ground 

Common Mode Follower / Custom

Head Size Range

Custom Caps

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

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


A team at University of Minnesota Twin Cities measured orientation selective adaptation by presenting plaid stimulus at different frequencies, wile recording EEG for ocipital visual areas

Image Luminance in Visual Cortex

A team at SUNY College of Optometry used EEG to validate that differences in light-dark contrast increase with luminance range and are largest in bright environments


74 entries « 2 of 8 »


Yu, Heeseung; Han, Eunkyoung

People see what they want to see: an EEG study Journal Article

In: Cognitive Neurodynamics, pp. 1–15, 2023.

Abstract | Links

Ferrisi, Leonardo M

Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input Masters Thesis


Abstract | Links

Demarest, Phillip; Rustamov, Nabi; Swift, James; Xie, Tao; Adamek, Markus; Cho, Hohyun; Wilson, Elizabeth; Han, Zhuangyu; Belsten, Alexander; Luczak, Nicholas; others,

A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study Journal Article

In: 2023.

Abstract | Links

Maffei, Luigi; Masullo, Massimiliano

Sens i-Lab: a key facility to expand the traditional approaches in experimental acoustics Journal Article

In: Institute of Noise Control Engineering, vol. 266, no. 2, pp. 134–140, 2023.

Abstract | Links

Lim, Hyunmi; Jeong, Chang Hyeon; Kang, Youn Joo; Ku, Jeonghun

Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients Journal Article

In: Cyberpsychology, Behavior, and Social Networking, 2023.

Abstract | Links

Seo, Seoung Won; Kim, Yong Seong

Stroke Patients: Effects of Combining Sitting Table Tennis Exercise with Neurological Physical Therapy on Brain Waves Journal Article

In: The Journal of Korean Physical Therapy, vol. 35, no. 1, pp. 19–23, 2023.

Abstract | Links

Chen, Sheng; Xie, Haiqun; Yang, Hongjun; Fan, Chenchen; Hou, Zengguang; Zhang, Chutian

A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments Journal Article

In: Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, pp. 349–361, 2023.

Abstract | Links

Kambhamettu, Sudhendra; Cruz, Meenalosini Vimal; Anitha, S; Chakkaravarthy, S Sibi; Kumar, K Nandeesh

Brain-Computer Interface-Assisted Automated Wheelchair Control Management--Cerebro: A BCI Application Journal Article

In: Brain-Computer Interface: Using Deep Learning Applications, pp. 205–229, 2023.

Abstract | Links


Dhaliwal, BS; Haddad, J; Debrincat, M; others,

Changes in Electroencephalogram (EEG) After Foot Stimulation with Embedded Haptic Vibrotactile Trigger Technology: Neuromatrix and Pain Modulation Considerations. Anesth Pain Res. 2022; 6 (2): 1-11 Journal Article

In: Correspondence: Peter Hurwitz, Clarity Science LLC, vol. 750, 2022.

Abstract | Links

Ocay, Don Daniel; Teel, Elizabeth F; Luo, Owen D; Savignac, Chloé; Mahdid, Yacine; Blain-Moraes, Stefanie; Ferland, Catherine E

Electroencephalographic characteristics of children and adolescents with chronic musculoskeletal pain Journal Article

In: PAIN Reports, vol. 7, no. 6, pp. e1054, 2022.

Abstract | Links

74 entries « 2 of 8 »