Brain Computer Interfaces (BCI)

BCI allows direct brain control of a wide range of computers and other devices including robots, wheelchairs, home appliances and drones by interpreting EEG signals and translating them into machine control commands.


An effective Brain-Computer Interface (BCI) leverages the separate strengths of both human and machine to create new capabilities or leaps in efficiencies. With B-Alert BCI development tools, developers are provided rapid prototyping tools to fit the right approach with the right task.

Within clinical environments, the results are recovery of lost function and accelerated healing. In other applications, BCIs facilitate more efficient interactions between man and machine.

BCIs applied to broader markets will benefit from the scalability of B-Alert’s core technology.


Wireless EEG enables interactive rehabilitation for neurological disorders and communication for locked in syndrome (e.g., ALS). BCI applications assess restructuring of neural pathways during recovery from stroke, TBI and spinal cord or other injury.

BBC horizon Profiles B-Alert Enabled BCI Technology

Prof. Paul Sajda and his team at Columbia University applied B-Alert X10 to elucidate the mechanisms of our brain’s powerful internal code when items of interest are identified. It serves as another example of B-Alert bridging the gap between neuroscience labs and real-world needs.

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An Adaptive Brain Actuated System for Augmenting Rehabilitation.
, Scott, et al. (2014). Frontiers in Neuroscience, 8, 415.
Changes in cortical activation during BCI use in chronic spinal cord injury.
Xie, Ziqian, et al. (2014). 6th International Brain-Computer Interface Conference, Graz, Austria.
Cognitive Skills Assessment during Robot-Assisted Surgery: Separating Wheat from Chaff.
Guru, Khurshid, et al. (2014). BJU international, 115(1).
In a Blink of an Eye and a Switch of a Transistor: Cortically Coupled Computer Vision.
Sajda, Paul, et al. (2010). Proceedings of the IEEE, 98, 462 - 478.
Non-invasive EEG-based motor and language mapping while playing a Kinetic based computer game.
Scherer, Reinhold, et al. (2013). IEEE Transactions on Computational Intelligence and AI in Games, 5, 155-163.
Dynamic Feature Selection in a Reinforcement Learning Brain Controlled FES.
Roset, S. (2014) Open Access Dissertations, Paper 1240.
Implementation of a Closed-Loop Real-Time EEG-Based Drowsiness Detection System: Effects of Feedback Alarms on Performance in a Driving Simulator.
Berka, Chris, et al. (2005). 1st International Conference on Augmented Cognition, Las Vegas, NV.
Effects of User Mental State on EEG-BCI Performance.
Myrden, A. et al. (2015). Frontiers in Human Neuroscience, 9(308), 1–11.
A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery.
Barngrover, C. et al. (2016).  IEEE Journal of Oceanic Engineering, 41(1), 123–138.
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