Nerve conduction and activity is a marker of disease and wellness and provides insight into the complex way the nervous system encodes information. We propose an electro-optical detection system and show the recordings from an electrically stimulated in-vitro nerve preparation. The system converts the action potential at the probing position to light intensity before any amplification and detection. Thence the light signal is detected by a photodetector. The new detection system has the ability of isolating the probing point and the amplification circuits, which reduces the electrical interference from the circuit. Moreover, the sampled signal transmitted via optical fibres rather than cables or wires makes it more robust to environmental noise. From the experiment, we demonstrated that the electro-optical detection system is able to detect and amplify the nerve response. By analysing the data, we can distinguish the response from the stimulus artifact and calculate CAP (compound action potential) propagation speed.
This paper presents an investigation into a novel electro-optic device for bi-directional brain-machine interface (BMI) by using both a chiral smectic C* liquid crystal to sense neuronal signals and the photovoltaic effect to stimulate neuronal tissues. By leveraging both the optical and electrical domains, this new electro-optic device can achieve high density of channel count and we have so far demonstrated up to 323 such channels. We focus here on tissue stimulation by adding a photovoltaic PN junction into the LC sensing structure described elsewhere to achieve a full bi-directional neuronal interface.
Multielectrode arrays are a powerful tool for recording biopotentials, however they are limited by issues related to wiring complexity and channel-count. We present a novel concept for a liquid crystal-based optical electrode (optrode) that does not require the electrical circuitry associated with reading and amplifying each channel, thus providing superior spatial resolution and signal-to-noise ratio. Through computational modeling, we show that it is possible to accurately image biopotentials by coupling them to the electrodes of a LC cell and measuring their re ectance under parallel polarisers.
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