Fabrication and Testbed Network for Next-Generation Optical Brain Computer Interfacing
University of Bath: Hannah Leese
University of Bristol: Daniel Whitcomb (PI)
Cardiff University: Sam Shutts
University of Exeter: Tom Piers
Background
Brain-computer interfacing (BCI) holds huge potential to transform how humans interact with computer-controlled systems. Applications range from thought-controlled electronics to human-machine decision making.
However, there are major technology road-blocks that still constrain the BCI application space. An effective ‘closed-loop’ BCI system – where computer systems and neural networks seamlessly communicate – require a means to read brain signals (the ‘input’ to computer systems), and a way to relay information back into the brain (via neural stimulation). Current approaches use metal electrodes, which stimulate and detect activity of populations of neural cells. However, electrodes are fundamentally limited. Firstly, only population-level signals are detected, failing to capture the significant signal heterogeneity between neurons. Secondly, stimulation is indiscriminately delivered to large groups of cells, limiting the ability to interact precisely with neural circuits. This means that current BCI approaches are unable to interface with complex neural circuitry, limiting effectiveness.
Project Summary
Our GW4 Community will scope and refine transformative research avenues to overcome these limitations. We have developed an initial concept to kick-start our Community: a novel all-optical signal acquisition and cell-stimulation platform with single-cell resolution. We envisage monolithic micro-LED (µ-LED) optrodes, formed of high-density light-emitting and light-detecting points. When integrated with neural cells engineered to fluoresce when active and to activate when exposed to light, the optrodes will seamlessly receive and send signals to neural networks, allowing complete computational integration with the brain.