Analogue Front-End Model for Developing Neural Spike Sorting Systems

A behavioural model for neural recording Analogue Front Ends (AFEs) based on a Matlab GUI. Source code available via Mathworks FileExchange.

For further details, see: Barsakcioglu D, Liu Y, Bhunjun P, Navajas J, Eftekhar A, Jackson A, Quian Quiroga R, Constandinou TG, 2014, An Analogue Front-End Model for Developing Neural Spike Sorting Systems, IEEE Transactions on Biomedical Circuits and Systems, Vol: 8, Pages: 216-227 

Bayesian Adaptive Kernel Smoother (BAKS)

BAKS is a method for estimating firing rate from spike train data that uses kernel smoothing technique with adaptive bandwidth determined using a Bayesian approach. Source code available on Github.

For further details, see: Ahmadi N, Constandinou TG, Bouganis C.-S., 2017, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), bioRxiv 204818.

Finite difference time domain bi-dimensional model for simulating Optical Coherence Tomography (OCT)

A finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT) for a myelinated peripheral nerve. Source code available on Github.

For further details, see: Troiani F, Nikolic K, Constandinou, TG, 2017, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, Plos One, Vol:14, ISSN:1932-6203.

Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging

We have developed the NGNI platform - an end-to-end solution for on-node, real-time spike sorting. By using a compact, onboard (template based) spike sorting engine, together with offline training (WaveClus-based), a low power real-time solution is achievable. Technical resources (code, PCB designs), user manual, etc available on GitHub.

For further details, see the NGNI resource webpage and bioRxiv pre-print: Luan S, Williams Y, Maslik M, Liu Y, Carvalho F, Jackson A, Quiroga RR, Constandinou, TG, 2017, Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging, bioRxiv:186627.

Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

An approx. 20 pound (GBP) open-source electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Hardware design files and software/firmware source code on Github.

For further details, see the NGNI resource webpage and bioRxiv pre-print: James Teversham, Steven Wong, Bryan Hsieh, Adrien Rapeaux, Francesca Troiani, Oscar Savolainen, Zheng Zhang, Michal Maslik, Timothy Constandinou, "Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding", bioRxiv 2022.01.29.478203; doi: https://doi.org/10.1101/2022.01.29.478203