Dry Sensor Interface

DSI-7

Dry - Mobile - Fast - EEG

Wearable Sensing’s wireless DSI-7 is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-7 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-7 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-7 has sensor locations covering the frontal, center, and posterior areas of the brain. There are 7 sensors whose locations can be customized upon order, and 2 earclip sensors. 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, 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-7 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

Customizable Locations

DSI-7 default sensor locations are shown in orange. Around each sensor location is a color map, representing a custom modification that can be made upon ordering to move the default sensor location to any location within the color map. Please note that once the sensor location is changed, it is fixed, and can only be moved if the system is sent back to our facility

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
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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.

Hardware

EEG Channels

Can be customized on demand by manufacturer

Reference / Ground 

Common Mode Follower / Fz

Head Size Range

Adult Size: 52cm – 62cm circumference
Child Size: 48cm – 54cm circumference

Sampling Rate

300 Hz (600Hz upgrade available)

Bandwidth

0.003 – 150 Hz

A/D resolution

0.317 μV referred to input

Input Impedance (1Hz)

47 GΩ

CMRR

> 120 dB

Amplifier / Digitizer

16 bits / 7 channels

Wireless

Bluetooth

Wireless Range

10 m

Run-time

> 12 Hours

Onboard Storage

~ 68 Hours (available option)

Software

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

Neurofeedback

 Brainmaster Brain Avatar; EEGer

Neuromarketing

CAPTIV Neurolab

Presentation

Presentation; E-Prime

Stroke Rehabilitation

Dr. Eric Leuthardt and his team at Neurolutions have developed the IpsiHand, and exoskeleton that is combined with the DSI-7 and BCI algorithms to provide upper extremity rehabilitation for chronic stroke patients

Predicting Hypoxia Using Machine Learning

Dr. Dallas H.Snider and his team at the Naval Medical Research Unit used the DSI-7 and machine learning algorithms on 85 pilots to indicate patterns of hypoxic hypoxia

Publications

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2022

Dong, Xian; Wu, Yeyu; Tu, Zhijun; Cao, Bin; Li, Xianting; Yang, Zixu; Liu, Fei; Xing, Zheli

Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering Journal Article

In: Energy and Buildings, pp. 112438, 2022.

Abstract | Links

Humphries, Joseph B; Mattos, Daniela JS; Rutlin, Jerrel; Daniel, Andy GS; Rybczynski, Kathleen; Notestine, Theresa; Shimony, Joshua S; Burton, Harold; Carter, Alexandre; Leuthardt, Eric C

Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface Journal Article

In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022.

Abstract | Links

Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C

Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke Journal Article

In: Brain Communications, vol. 4, iss. 3, 2022.

Abstract | Links

2021

Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G

Predicting hypoxic hypoxia using machine learning and wearable sensors Journal Article

In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021.

Abstract | Links

Swerdloff, Margaret M; Hargrove, Levi J

Identifying the Onset of Increased Cognitive Load using Event-Related Potentials in Electroencephalography Conference

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE 2021, ISBN: 978-1-7281-4338-5.

Abstract | Links

Rahimi-Nasrabadi, Hamed; Jin, Jianzhong; Mazade, Reece; Pons, Carmen; Najafian, Sohrab; Alonso, Jose-Manuel

Image luminance changes contrast sensitivity in visual cortex Journal Article

In: Cell reports, vol. 34, no. 5, pp. 108692, 2021.

Abstract | Links

2020

Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S

A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference

2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7.

Abstract | Links

2019

Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn

A Test Setting to Compare Spatial Awareness on Paper and in Virtual Reality Using EEG Signals Conference

International Conference on Applied Human Factors and Ergonomics, Springer 2019.

Abstract | Links

Rice, Merrill G; Snider, Dallas; Drollinger, Sabrina; Greil, Chris; Bogni, Frank; Phillips, Jeffrey; Raj, Anil; Marco, Katherine; Linnville, Steven

Gender Differences in Dry-EEG Manifestations During Acute and Insidious Normobaric Hypoxia Journal Article

In: Aerospace Medicine and Human Performance, vol. 90, no. 4, pp. 369–377, 2019.

Abstract | Links

Flumeri, Gianluca Di; Aric`o, Pietro; Borghini, Gianluca; Sciaraffa, Nicolina; Florio, Antonello Di; Babiloni, Fabio

The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability Journal Article

In: Sensors, vol. 19, no. 6, pp. 1365, 2019.

Abstract | Links

13 entries « 1 of 2 »