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.

Overview

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 with the potential to transition to broader markets will benefit from the scalability of B-Alert’s core technology

Neurorehabilitation

Wireless EEG enables rehabilitation for neurological disorders and communication for locked in syndrome (e.g., ALS). BCI applications assess restructuring of neural pathways during stroke, TBI and spinal cord or other injury by providing feedback based on error-related neural signatures to reinforce learning or to exert control of functional electrical stimulation

BBC horizon Profiles B-Alert Enabled BCI Technology

Prof. Paul Sajda (Neuromatters) 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.

Learn More

An Adaptive Brain Actuated System for Augmenting Rehabilitation. Roset, Scott & Gant, Katie & Prasad, Abhishek & Sanchez, Justin. (2014). Frontiers in neuroscience. 8. 415. 10.3389/fnins.2014.00415.
Changes in cortical activation during BCI use in chronic spinal cord injury. Xie, Ziqian & Korszen, Stephanie & Gant, Katie & Sanchez, Justin & Berka, Chris & Prasad, Abhishek. (2014).
Cognitive Skills Assessment during Robot-Assisted Surgery: Separating Wheat from Chaff. Guru, Khurshid & Tarkesh Esfahani, Ehsan & Raza, Syed & Bhat, Rohit & Wang, Katy & Hammond, Yana & Wilding, Greg & Peabody, James & Chowriappa, Ashirwad. (2014). BJU international. 115. 10.1111/bju.12657.
In a Blink of an Eye and a Switch of a Transistor: Cortically Coupled Computer Vision. Sajda, Paul & Pohlmeyer, Eric & Wang, Jun & Parra, Lucas & Christoforou, Christoforos & Dmochowski, Jacek & Hanna, Barbara & Bahlmann, Claus & Singh, Maneesh & Chang, S.. (2010). Proceedings of the IEEE. 98. 462 - 478. 10.1109/JPROC.2009.2038406.
Non-invasive EEG-based motor and language mapping while playing a Kinetic based computer game. Scherer, Reinhold & Moitzi, Gunter & Daly, Ian & Müller-Putz, Gernot. (2013). Computational Intelligence and AI in Games, IEEE Transactions on. 5. 155-163. 10.1109/TCIAIG.2013.2250287.
Dynamic Feature Selection in a Reinforcement Learning Brain Controlled FES. Roset, S. Open Access Dissertations. 1240. (2014)
Implementation of a Closed-Loop Real-Time EEG-Based Drowsiness Detection System: Effects of Feedback Alarms on Performance in a Driving Simulator. Berka, Chris & Levendowski, Daniel & Westbrook, Philip & Davis, Gene & Lumicao, Michelle & Olmstead, Richard & Popovic, Miodrag & Zivkovic, Tristan & Ramsey, Caitlin. (2005).
Effects of User Mental State on EEG-BCI Performance. Frontiers in Human Neuroscience. Myrden, A., & Chau, T. (2015). 9(308), 1–11;
A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery. Barngrover, C., Althoff, A., DeGuzman, P., & Kastner, R. (2016).  IEEE Journal of Oceanic Engineering, 41(1), 123–138. 
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