Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces

The Automation of Electrophysiological Experiments and Data Analysis

The role of computation in science is continually growing and neuroscience is no exception. Despite this, a severe lack of scientific software infrastructure persists, slowing progress in many domains. In this thesis, we will see how the combination of neuroscience and software engineering can build infrastructure that enables discovery. The first chapter discusses the Turtle Electrophysiology Project, or TEP, an experiment-automation and data-management system. This system has allowed us to automate away some of the most tedious tasks involved in conducting experiments. As a result, we can collect more data in less time, and with fewer errors related to the loss of metadata: information about how the data were collected). Also, since all of the metadata is automatically digitized during the experiment we can now completely automate our analyses. Chapters two and three are examples of research conducted using the ever-evolving TEP system. In the first instance, we used TEP to deliver visual stimuli and handle data-management. In chapter three, the experiments involved delivering electrical stimuli instead of visual stimuli, and much more rigorous analysis. And even though TEP was not specifically designed to handle collecting data this way, the flexible tags system enabled us to do so. Finally, chapter four details the construction of a robust analysis tool called Spikepy. Whereas TEP is specially designed for the turtle preparation we have, Spikepy is a general-purpose spike-sorting application and framework. Spikepy takes flexibility to the extreme by being a plugin-based framework, yet maintaining a very easy to use interface

Investigating information processing within the brain using multi-electrode array (MEA) electrophysiology data

How a stimulus, such as an odour, is represented in the brain is one of the main questions in neuroscience. It is becoming clearer that information is encoded by a population of neurons, but, how the spiking activity of a population of neurons conveys this information is unknown. Several population coding hypotheses have formulated over the years, and therefore, to obtain a more definitive answer as to how a population of neurons represents stimulus information we need to test, i.e. support or falsify, each of the hypotheses. One way of addressing these hypotheses is to record and analyse the activity of multiple individual neurons from the brain of a test subject when a stimulus is, and is not, presented. With the advent of multi electrode arrays (MEA) we can now record such activity. However, before we can investigate/test the population coding hypotheses using such recordings, we need to determine the number of neurons recorded by the MEA and their spiking activity, after spike detection, using an automatic spike sorting algorithm (we refer to the spiking activity of the neurons extracted from the MEA recordings as MEA sorted data). While there are many automatic spike sorting methods available, they have limitations. In addition, we are lacking methods to test/investigate the population coding hypotheses in detail using the MEA sorted data. That is, methods that show whether neurons respond in a hypothesised way and, if they do, shows how the stimulus is represented within the recorded area. Thus, in this thesis, we were motivated to, firstly, develop a new automatic spike sorting method, which avoids the limitations of other methods. We validated our method using simulated and biological data. In addition, we found our method can perform better than other standard methods. We next focused on the population rate coding hypothesis (i.e. the hypothesis that information is conveyed in the number of spikes fired by a pop- ulation of neurons within a relevant time period). More specifically, we developed a method for testing/investigating the population rate coding hypothesis using the MEA sorted data. That is, a method that uses the multi variate analysis of variance (MANOVA) test, where we modified its output, to show the most responsive subar- eas within the recorded area. We validated this using simulated and biological data. Finally, we investigated whether noise correlation between neurons (i.e. correlations in the trial to trial variability of the response of neurons to the same stimulus) in a rat's olfactory bulb can affect the amount of information a population rate code conveys about a set of stimuli. We found that noise correlation between neurons was predominately positive, which, ultimately, reduced the amount of information a population containing >45 neurons could convey about the stimuli by ~30%

Decoding Information From Neural Signals Recorded Using Intraneural Electrodes: Toward the Development of a Neurocontrolled Hand Prosthesis

The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this achievement. Among the various approaches, peripheral nerve based intrafascicular electrodes are excellent neural interface candidates, representing an excellent compromise between high selectivity and relatively low invasiveness. Moreover, this approach has undergone preliminary testing in human volunteers and has shown promise. In this paper, we investigate whether the use of intrafascicular electrodes can be used to decode multiple sensory and motor information channels with the aim to develop a finite state algorithm that may be employed to control neuroprostheses and neurocontrolled hand prostheses. The results achieved both in animal and human experiments show that the combination of multiple sites recordings and advanced signal processing techniques (such as wavelet denoising and spike sorting algorithms) can be used to identify both sensory stimuli (in animal models) and motor commands (in a human volunteer). These findings have interesting implications, which should be investigated in future experiments. © 2006 IEEE

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy