15 results found
Luan S, Williams I, Maslik M, et al., 2018, Compact standalone platform for neural recording with real-time spike sorting and data logging., J Neural Eng, Vol: 15
OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain-machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation. APPROACH: We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. MAIN RESULTS: The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. SIGNIFICANCE: The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable outp
Williams I, 2018, Zero mean waveforms for neural stimulation
Biphasic charge balanced waveforms do not minimise faradaic processes at the electrode-electrolyte boundary and do not leave electrodes neutral with respect to the tissue. Superior waveforms for electrode health (and consequently tissue safety) exist and may also offer better performance in terms of power consumption and stimulation effectiveness within charge injection limits. This paper aims to provide intuitive insight into the limitations of biphasic waveforms and presents a simple method for assessing how well other waveforms will perform, as well as methods for designing waveforms to theoretically give zero residual voltage and zero net faradaic charge transfer.
Williams I, Leene L, Constandinou TG, 2018, Next Generation Neural Interface Electronics, Circuit Design Considerations for Implantable Devices, Editors: Cong, Publisher: River Publishers, Pages: 141-178, ISBN: 978-87-93519-86-2
Williams I, Rapeaux A, Luan S, et al., 2018, Waveform Generator
Liu Y, Luan S, Williams I, et al., 2017, A 64-Channel Versatile Neural Recording SoC With Activity-Dependent Data Throughput, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 11, Pages: 1344-1355, ISSN: 1932-4545
Luan S, Williams I, De-Carvalho F, et al., 2017, Standalone headstage for neural recording with real-time spike sorting and data logging, BNA Festival of Neuroscience, Publisher: The British Neuroscience Association Ltd
Frehlick Z, Williams I, Constandinou TG, 2016, Improving Neural Spike Sorting Performance using Template Enhancement, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 524-527, ISSN: 2163-4025
Luan S, Liu Y, Williams I, et al., 2016, An Event-Driven SoC for Neural Recording, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 404-407, ISSN: 2163-4025
Luan S, Williams I, de Carvalho F, et al., 2016, Next Generation Neural Interfaces for low-power multichannel spike sorting, FENS Forum of Neuroscience, Publisher: FENS
Williams I, Rapeaux A, Liu Y, et al., 2016, A 32-Ch. Bidirectional Neural/EMG Interface with on-Chip Spike Detection for Sensorimotor Feedback, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 528-531, ISSN: 2163-4025
Rapeaux A, Nikolic K, Williams I, et al., 2015, Fiber Size-Selective Stimulation using Action Potential Filtering for a Peripheral Nerve Interface: A Simulation Study, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 3411-3414, ISSN: 1557-170X
Williams I, Luan S, Jackson A, et al., 2015, A Scalable 32 Channel Neural Recording and Real-time FPGA Based Spike Sorting System, 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 188-191, ISSN: 2163-4025
Neuromodulation has wide ranging potential applications in replacing impaired neural function (prosthetics), as a novel form of medical treatment (therapy), and as a tool for investigating neurons and neural function (research). Voltage and current controlled electrical neural stimulation (ENS) are methods that have already been widely applied in both neuroscience and clinical practice for neuroprosthetics. However, there are numerous alternative methods of stimulating or inhibiting neurons. This paper reviews the state-of-the-art in ENS as well as alternative neuromodulation techniques-presenting the operational concepts, technical implementation and limitations-in order to inform system design choices.
Williams I, Constandinou TG, 2014, Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study, FRONTIERS IN NEUROSCIENCE, Vol: 8, ISSN: 1662-453X
Williams I, Constandinou TG, 2013, An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 7, Pages: 129-139, ISSN: 1932-4545
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.