An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes

Unsupervised neural spike identification for large-scale, high-density micro-electrode arrays

This work deals with the development and evaluation of algorithms that extract sequences of single neuron action potentials from extracellular recordings of superimposed neural activity - a task commonly referred to as spike sorting. Large ($>10^3$ electrodes) and dense (subcellular spatial sampling) CMOS-based micro-electrode-arrays allow to record from hundreds of neurons simultaneously. State of the art algorithms for up to a few hundred sensors are not directly applicable to this type of data. Promising modern spike sorting algorithms that seek the statistically optimal solution or focus on real-time capabilities need to be initialized with a preceding sorting. Therefore, this work focused on unsupervised solutions, in order to learn the number of neurons and their spike trains with proper resolution of both temporally and spatiotemporally overlapping activity from the extracellular data alone. Chapter (1) informs about the nature of the data, a model based view and how this relates to spike sorting in order to understand the design decisions of this thesis. The main materials and methods chapter (2) bundles the infrastructural work that is independent of but mandatory for the development and evaluation of any spike sorting method. The main problem was split in two parts. Chapter (3) assesses the problem of analyzing data from thousands of densely integrated channels in a divide-and-conquer fashion. Making use of the spatial information of dense 2D arrays, regions of interest (ROIs) with boundaries adapted to the electrical image of single or multiple neurons were automatically constructed. All ROIs could then be processed in parallel. Within each region of interest the maximum number of neurons could be estimated from the local data matrix alone. An independent component analysis (ICA) based sorting was used to identify units within ROIs. This stage can be replaced by another suitable spike sorting algorithm to solve the local problem. Redundantly identified units across different ROIs were automatically fused into a global solution. The framework was evaluated on both real as well as simulated recordings with ground truth. For the latter it was shown that a major fraction of units could be extracted without any error. The high-dimensional data can be visualized after automatic sorting for convenient verification. Means of rapidly separating well from poorly isolated neurons were proposed and evaluated. Chapter (4) presents a more sophisticated algorithm that was developed to solve the local problem of densely arranged sensors. ICA assumes the data to be instantaneously mixed, thereby reducing spatial redundancy only and ignoring the temporal structure of extracellular data. The widely accepted generative model describes the intracellular spike trains to be convolved with their extracellular spatiotemporal kernels. To account for the latter it was assessed thoroughly whether convolutive ICA (cICA) could increase sorting performance over instantaneous ICA. The high computational complexity of cICA was dealt with by automatically identifying relevant subspaces that can be unmixed in parallel. Although convolutive ICA is suggested by the data model, the sorting results were dominated by the post-processing for realistic scenarios and did not outperform ICA based sorting. Potential alternatives are discussed thoroughly and bounded from above by a supervised sorting. This work provides a completely unsupervised spike sorting solution that enables the extraction of a major fraction of neurons with high accuracy and thereby helps to overcome current limitations of analyzing the high-dimensional datasets obtained from simultaneously imaging the extracellular activity from hundreds of neurons with thousands of electrodes

Analog VLSI Circuits for Biosensors, Neural Signal Processing and Prosthetics

Stroke, spinal cord injury and neurodegenerative diseases such as ALS and Parkinson's debilitate their victims by suffocating, cleaving communication between, and/or poisoning entire populations of geographically correlated neurons. Although the damage associated with such injury or disease is typically irreversible, recent advances in implantable neural prosthetic devices offer hope for the restoration of lost sensory, cognitive and motor functions by remapping those functions onto healthy cortical regions. The research presented in this thesis is directed toward developing enabling technology for totally implantable neural prosthetics that could one day restore lost sensory, cognitive and motor function to the victims of debilitating neural injury or disease. There are three principal components to this work. First, novel integrated biosensors have been designed and implemented to transduce weak extra-cellular electrical potentials and optical signals from cells cultured directly on the surface of the sensor chips, as well as to manipulate cells on the surface of these chips. Second, a method of detecting and identifying stereotyped neural signals, or action potentials, has been mapped into silicon circuits which operate at very low power levels suitable for implantation. Third, as one small step towards the development of cognitive neural implants, a learning silicon synapse has been implemented and a neural network application demonstrated. The original contributions of this dissertation include: * A contact image sensor that adapts to background light intensity and can asynchronously detect statistically significant optical events in real-time; * Programmable electrode arrays for enhanced electrophysiological recording, for directing cellular growth, for site-specific in situ bio-functionalization, and for analyte and particulate collection; * Ultra-low power, programmable floating gate template matching circuits for the detection and classification of neural action potentials; * A two transistor synapse that exhibits spike timing dependent plasticity and can implement adaptive pattern classification and silicon learning

Applications of clustering analysis to signal processing problems.

Wing-Keung Sim.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 109-114).Abstracts in English and Chinese.Abstract --- p.2摘要 --- p.3Acknowledgements --- p.4Contents --- p.5List of Figures --- p.8List of Tables --- p.9Introductions --- p.10Chapter 1.1 --- Motivation & Aims --- p.10Chapter 1.2 --- Contributions --- p.11Chapter 1.3 --- Structure of Thesis --- p.11Electrophysiological Spike Discrimination --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Cellular Physiology --- p.13Chapter 2.2.1 --- Action Potential --- p.13Chapter 2.2.2 --- Recording of Spikes Activities --- p.15Chapter 2.2.3 --- Demultiplexing of Multi-Neuron Recordings --- p.17Chapter 2.3 --- Application of Clustering for Mixed Spikes Train Separation --- p.17Chapter 2.3.1 --- Design Principles for Spike Discrimination Procedures --- p.17Chapter 2.3.2 --- Clustering Analysis --- p.18Chapter 2.3.3 --- Comparison of Clustering Techniques --- p.19Chapter 2.4 --- Literature Review --- p.19Chapter 2.4.1 --- Template Spike Matching --- p.19Chapter 2.4.2 --- Reduced Feature Matching --- p.20Chapter 2.4.3 --- Artificial Neural Networks --- p.21Chapter 2.4.4 --- Hardware Implementation --- p.21Chapter 2.5 --- Summary --- p.22Correlation of Perceived Headphone Sound Quality with Physical Parameters --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Sound Quality Evaluation --- p.23Chapter 3.3 --- Headphone Characterization --- p.26Chapter 3.3.1 --- Frequency Response --- p.26Chapter 3.3.2 --- Harmonic Distortion --- p.26Chapter 3.3.3 --- Voice-Coil Driver Parameters --- p.27Chapter 3.4 --- Statistical Correlation Measurement --- p.29Chapter 3.4.1 --- Correlation Coefficient --- p.29Chapter 3.4.2 --- t Test for Correlation Coefficients --- p.30Chapter 3.5 --- Summary --- p.31Algorithms --- p.32Chapter 4.1 --- Introduction --- p.32Chapter 4.2 --- Principal Component Analysis --- p.32Chapter 4.2.1 --- Dimensionality Reduction --- p.32Chapter 4.2.2 --- PCA Transformation --- p.33Chapter 4.2.3 --- PCA Implementation --- p.36Chapter 4.3 --- Traditional Clustering Methods --- p.37Chapter 4.3.1 --- Online Template Matching (TM) --- p.37Chapter 4.3.2 --- Online Template Matching Implementation --- p.40Chapter 4.3.3 --- K-Means Clustering --- p.41Chapter 4.3.4 --- K-Means Clustering Implementation --- p.44Chapter 4.4 --- Unsupervised Neural Learning --- p.45Chapter 4.4.1 --- Neural Network Basics --- p.45Chapter 4.4.2 --- Artificial Neural Network Model --- p.46Chapter 4.4.3 --- Simple Competitive Learning (SCL) --- p.47Chapter 4.4.4 --- SCL Implementation --- p.49Chapter 4.4.5 --- Adaptive Resonance Theory Network (ART). --- p.50Chapter 4.4.6 --- ART2 Implementation --- p.53Chapter 4.6 --- Summary --- p.55Experimental Design --- p.57Chapter 5.1 --- Introduction --- p.57Chapter 5.2 --- Electrophysiological Spike Discrimination --- p.57Chapter 5.2.1 --- Experimental Design --- p.57Chapter 5.2.2 --- Extracellular Recordings --- p.58Chapter 5.2.3 --- PCA Feature Extraction --- p.59Chapter 5.2.4 --- Clustering Analysis --- p.59Chapter 5.3 --- Correlation of Headphone Sound Quality with physical Parameters --- p.61Chapter 5.3.1 --- Experimental Design --- p.61Chapter 5.3.2 --- Frequency Response Clustering --- p.62Chapter 5.3.3 --- Additional Parameters Measurement --- p.68Chapter 5.3.4 --- Listening Tests --- p.68Chapter 5.3.5 --- Confirmation Test --- p.69Chapter 5.4 --- Summary --- p.70Results --- p.71Chapter 6.1 --- Introduction --- p.71Chapter 6.2 --- Electrophysiological Spike Discrimination: A Comparison of Methods --- p.71Chapter 6.2.1 --- Clustering Labeled Spike Data --- p.72Chapter 6.2.2 --- Clustering of Unlabeled Data --- p.78Chapter 6.2.3 --- Remarks --- p.84Chapter 6.3 --- Headphone Sound Quality Control --- p.89Chapter 6.3.1 --- Headphones Frequency Response Clustering --- p.89Chapter 6.3.2 --- Listening Tests --- p.90Chapter 6.3.3 --- Correlation with Measured Parameters --- p.90Chapter 6.3.4 --- Confirmation Listening Test --- p.92Chapter 6.4 --- Summary --- p.93Conclusions --- p.97Chapter 7.1 --- Future Work --- p.98Chapter 7.1.1 --- Clustering Analysis --- p.98Chapter 7.1.2 --- Potential Applications of Clustering Analysis --- p.99Chapter 7.2 --- Closing Remarks --- p.100Appendix --- p.101Chapter A.1 --- Tables of Experimental Results: (Spike Discrimination) --- p.101Chapter A.2 --- Tables of Experimental Results: (Headphones Measurement) --- p.104Bibliography --- p.109Publications --- p.11

The Design and Implementation of an Extensible Brain-Computer Interface

An implantable brain computer interface: BCI) includes tissue interface hardware, signal conditioning circuitry, analog-to-digital conversion: ADC) circuitry and some sort of computing hardware to discriminate desired waveforms from noise. Within an experimental paradigm the tissue interface and ADC hardware will rarely change. Recent literature suggests it is often the specific implementation of waveform discrimination that can limit the usefulness and lifespan of a particular BCI design. If the discrimination techniques are implemented in on-board software, experimenters gain a level of flexibility not currently available in published designs. To this end, I have developed a firmware library to acquire data sampled from an ADC, discriminate the signal for desired waveforms employing a user-defined function, and perform arbitrary tasks. I then used this design to develop an embedded BCI built upon the popular Texas Instruments MSP430 microcontroller platform. This system can operate on multiple channels simultaneously and is not fundamentally limited in the number of channels that can be processed. The resulting system represents a viable platform that can ease the design, development and use of BCI devices for a variety of applications

Modeling and Analysis of Neural Spike Trains

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From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript

The Development of Computer-Assisted Techniques for the Classification of Nerve Spike Signals

Since the development of electronic amplification and signal recording facilities, there has been considerable interest in separating and classifying nerve spike events. Initially techniques were developed to identify spikes on the basis of amplitude but, as technology has progressed, the main interest has been in the development of techniques for classifying nerve spikes on the basis of shape. A range of strategies have been developed for performing the separation, but these strategies have been (and possibly still are) limited by the capabilities of the available hardware. These strategies and their implementations are described. A novel method for performing automated spike shape classification is described. Software has been written to implement this method, and it is applied to nerve spike data from extracellular recordings of the superficial flexor nerve of the Norway lobster (Nephrops norvegicus) and from the coxo-basal chordotonal organ and cuticular stress detector one of the crayfish (Procambarus clarkii). The results are assessed by the use of contemporaneous intracellular recordings and compared with the performance of a commercially available spike classifier, voltage thresholding techniques and an implementation of a pre-existing technique for classifying spikes. The relative merits of different strategies are considered, as well as the fundamental limitations of attempting to segregate spike data on the basis of shape alone. Technical issues relating to the implementation of a software based spike classifier are also considered

Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces

Intracortical Brain-Machine Interfaces (iBMIs) have advanced significantly over the past two decades, demonstrating their utility in various aspects, including neuroprosthetic control and communication. To increase the information transfer rate and improve the devices’ robustness and longevity, iBMI technology aims to increase channel counts to access more neural data while reducing invasiveness through miniaturisation and avoiding percutaneous connectors (wired implants). However, as the number of channels increases, the raw data bandwidth required for wireless transmission also increases becoming prohibitive, requiring efficient on-implant processing to reduce the amount of data through data compression or feature extraction. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30% with minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30\% with only minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget.Open Acces

Automatic Spike sorting and robust power line interference cancellation for neural signal processing

Ph.DDOCTOR OF PHILOSOPH