Brain Computer Interfaces

Unlock the Potential in Everyone

EEG-based Brain-Computer Interfaces (BCI) is a non-invasive technique used to translate brain activity to commands that control an effector (such as a computer keyboard, mouse, etc).  Many patients who cannot communicate effectively, such as those who have suffered from a stroke, locked-in syndrome, or other neurodegenerative diseases, rely on BCI’s to stay connected. A few of the most common types of BCI’s modalities are P300, SSVEP,  slow cortical potentials, and sensorimotor rhythms. With Wearable Sensing’s revolutionary dry EEG technology, nearly any type of BCI is possible with our research-grade signal quality. Since DSI systems are extremely easy to use and comfortable, this has opened the door to translating a wide range of BCI applications to the real- and virtual- worlds.


P300 Speller

P300, otherwise known as the oddball paradigm, is an event-related potential (ERP) in which the brain elicits a unique response roughly 300ms after an “odd” stimulus is presented. This response can be decoded and classified in real-time for a variety of different applications.

One such use case is known as a P300 speller, in which a series of letters are flashed on a screen, and when the “target” letter pops up, our brain has the P300 response, which can then be transformed into a letter selection.

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.

SSVEP-Based Keyboard

Steady State Visually Evoked Potentials (SSVEP) are natural responses to visual stimuli at specific frequencies. In a typical SSVEP paradigm, targets will flash at differing frequencies, anywhere from 3.5 Hz – 75 Hz, and depending on which target the subject is attending to, the brain will have a characterizable response at such specific frequency.

As shown in the video, a 12 target numbered keyboard is setup, and the subject is counting up. There is no training required, and the algorithm can correctly classify in under 1 second, in some cases.

This specific SSVEP software was developed by Wearable Sensing’s collaborater in China, Neuracle, and is available for purchase for all DSI systems. The software comes ready to use, with customizable 12-count and 40-count keyboards designed for ultra-rapid, high-accuracy classification.  

Motor Imagery at Art Exhibit

Motor Imagery is a BCI technique in which the subject imagines performing a movement with a particular limb. This then alters the rhythmic activity in locations in the sensorimotor cortex that correspond to the imagined limb. The BCI can decode these signals, and translate the imagined movement into feedback in the form of cursor movements or other computer commands.

The DSI-24 was featured at an interactive art installation “Mental Work” at the Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland. During the exhibit, subjects were presented with a wheel that was controlled by the subject thinking about moving either one of their arms. 

Stroke Rehabilitation

Neurolutions is a medical device company developing neuro-rehabilitation solutions that seek to restore function to patients who are disabled as a result of neurological injury. The Neurolutions IpsiHand system provides upper extremity rehabilitation for chronic stroke patients leveraging brain-computer interface and advanced wearable robotics technology.

By utilizing the DSI-7, Neurolutions is able to use Motor Imagery techniques to decode a patients intent to move their finger, which then instructs the exoskeleton to physically move the finger. With repeated sessions, patients can regain control of their lost limbs.

What fires together, wires together!



All of Wearable Sensing's Dry EEG systems can be utilized for Brain Computer Interfaces

NeuSenW High-Density EEG

All of Wearable Sensing's Wet EEG systems can be utilized for Brain Computer Interfaces


SSVEP BCI Algorithms

Software platform that enables user friendly SSVEP paradigm for BCI applications and research of SSVEP

3rd Party Compatible Software

List of compatible software, including Neurofeedback, BCI, EEG Analysis, SDK's, and more


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Rueda-Parra, S.; Hardesty, R. L.; Gemoets, D.; Hill, J.; Gupta, D.

Reliability testing of EEG spectral features in a robot-based arm movement task Conference

Society for Neuroscience 2024.

Abstract | Links | BibTeX

Jeong, Chang Hyeon; Lim, Hyunmi; Lee, Jiye; Lee, Hye Sun; Ku, Jeonghun; Kang, Youn Joo

Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke Journal Article

In: Frontiers in Neuroscience, vol. 18, pp. 1373589, 2024.

Abstract | Links | BibTeX

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 | BibTeX


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 | BibTeX

Ferrisi, Leonardo M

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


Abstract | Links | BibTeX

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 | BibTeX


Won, Kyungho; Kim, Heegyu; Gwon, Daeun; Ahn, Minkyu; Nam, Chang S; Jun, Sung Chan

Can Vibrotactile Stimulation and tDCS Help Inefficient BCI Users? Journal Article

In: 2022.

Abstract | Links | BibTeX

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 | BibTeX

Kim, Min Gyu; Lim, Hyunmi; Lee, Hye Sun; Han, In Jun; Ku, Jeonghun; Kang, Youn Joo

Brain--computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients Journal Article

In: Journal of Neural Engineering, vol. 19, no. 3, 2022.

Abstract | Links | BibTeX

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 | BibTeX

24 entries « 1 of 3 »


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