Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning

This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of {-1, 0 and 1} and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels $\sigma_N$ between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm2 and dissipates up to about 10.48 μW from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency

Neural activity classification with machine learning models trained on interspike interval series data

The flow of information through the brain is reflected by the activity patterns of neural cells. Indeed, these firing patterns are widely used as input data to predictive models that relate stimuli and animal behavior to the activity of a population of neurons. However, relatively little attention was paid to single neuron spike trains as predictors of cell or network properties in the brain. In this work, we introduce an approach to neuronal spike train data mining which enables effective classification and clustering of neuron types and network activity states based on single-cell spiking patterns. This approach is centered around applying state-of-the-art time series classification/clustering methods to sequences of interspike intervals recorded from single neurons. We demonstrate good performance of these methods in tasks involving classification of neuron type (e.g. excitatory vs. inhibitory cells) and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep states) on an open-access cortical spiking activity dataset

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

Ph.DDOCTOR OF PHILOSOPH

A Deep Neural Network-Based Spike Sorting with Improved Channel Selection and Artefact Removal

In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multi-unit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts

Structured Dictionary Learning and its applications in Neural Recording

Widely utilized in the field of neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored, particularly towards exploring a more efficient representation of the neural signals. As a promising solution, sparse representation not only provides better signal compression for bandwidth/storage efficiency, but also leads to faster processing algorithms as well as more effective signal separation for classification purpose. However, current sparsity‐based approaches for neural recording are limited due to several critical drawbacks: (i) the lack of an efficient data‐driven representation to fully capture the characteristics of specific neural signal; (ii) most existing methods do not fully explore the prior knowledge of neural signals (e.g., labels), while such information is often known; and (iii) the capability to encode discriminative information into the representation to promote classification. Using neural recording as a case study, this dissertation presents new theoretical ideas and mathematical frameworks on structured dictionary learning with applications in compression and classification. Start with a single task setup, we provide theoretical proofs to show the benefits of using structured sparsity in dictionary learning. Then we provide various novel models for the representation of a single measurement, as well as multiple measurements where signals exhibit both with‐in class similarity as well as with‐in class difference. Under the assumption that the label information of the neural signal is known, the proposed models minimize the data fidelity term together with the structured sparsity terms to drive for more discriminative representation. We demonstrate that this is particularly essential in neural recording since it can further improve the compression ratio, classification accuracy and help deal with non‐ideal scenarios such as co-occurrences of neuron firings. Fast and efficient algorithms based on Bayesian inference and alternative direction method are proposed. Extensive experiments are conducted on both neural recording applications as well as some other classification task, such as image classification

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

Developing robust movement decoders for local field potentials

textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin

Development of an analysis pipeline for human microelectrode recordings in Parkinson’s disease

Thesis to obtain the master degree in Biomedical EngineeringBackground: Deep brain stimulation is a common treatment for advanced Parkinson’s Disease (PD). Intraoperative microelectrode recordings (MER) along preplanned trajectories are often used for accurate identification of subthalamic nucleus (STN), a common target for deep brain stimulation (DBS) in PD. However, this identification is performed manually and can be difficult in regions of transition. Misidentification may lead to suboptimal location of the DBS lead and inadequate clinical outcomes. Methods: A tool for unsupervised analysis and spike-sorting of human MER signals with feature extraction was developed. We also trained and tested a hybrid unsupervised/supervised machine learning approach that uses extracted MER time, frequency and noise properties for high-accuracy identification of STN. Lastly, we compared neurophysiological characteristics of different STN functional segments. Results: We obtained a classification accuracy of 96:28 3:15 % (30 trajectories, 5 patients) for individual STN-DBS surgery MER using an approach of "leave one subject out" validation with support vector machine classifier, all features based on time and frequency domain and human expert labels. The unsupervised sorting approach allowed us to sort a total of 357 STN neurons in 5 subjects. Dividing the STN in a dorsal, probably motor region, and a ventral, probably non-motor portion, we’ve found a higher burst rate (median (interquartile range) of 1.8 (1.5) vs 1.15 (0.05) bursts/s, p=0.001) and firing rate (median (interquartile range) of 21.4 (16.85) vs. 15.3 (14.33), p=0.013) of dorsal STN neurons among other features. Ongoing work will refine these results using anatomical gold standard through lead trajectory reconstruction, fused with an STN functional subdivision atlas. Conclusions: We’ve developed a tool for human MER analysis and extraction of related features, that provided good preliminary results in STN classification. In line with the literature, we were able to find preliminary activity differences in functionally segregated STN segments. This tool is fast and generalizable for other brain regions. Ongoing work using patient’s anatomy can further validate its’ usefulness in optimizing electrode placement and research purposes.A Estimulação Cerebral Profunda é um técnica utilizada para o tratamento dos sintomas motores da Doença de Parkinson com recurso a estimulação eléctrica intracraniana. Um dos alvos mais frequentemente utilizados para o tratamento é o núcleo subtalâmico e uma colocação precisa dos eléctrodos na região correcta é fulcral para o sucesso terapêutico e minimização de efeitos secundários. Esta cirurgia faz-se com recurso a estereotaxia e monitorização intra-operatória. Previamente à cirurgia, as coordenadas do alvo são estabelecidas e é definido o núcleo subtalâmico utilizando imagens pré-operatórias de ressonância magnética e de tomografia computadorizada do doente. Intra-operatoriamente são utilizados registos de microeléctrodos a diferentes profundidades em trajetórias pré-definidas para verificar as coordenadas estabelecidas e identificar -pela actividade eléctrica- a localização dos eléctrodos no cérebro. No entanto, a identificação do núcleo subtalâmico com registos de microeléctrodos é frequentemente realizada por inspeção visual, e pode ser difícil em regiões de transição. Erros na identificação podem levar a um posicionamento subóptimo dos elétrodos e a um mau resultado clínico. O correcto posicionamento do eléctrodo final de estimulação na porção sensoriomotora do núcleo subtalâmico está relacionado com os melhores resultados clínicos de acordo com a literatura. No entanto não é possível identificar claramente esta subdivisão com base a inspecção visual dos registos. O objetivo do presente estudo é o desenvolvimento de ferramentas para análise de registos de microeléctrodos em diferentes áreas do cérebro e identificação do núcleo subtalâmico, e da porção sensoriomotora, em doentes com Doença de Parkinson submetidos a estimulação cerebral profunda. Métodos Foram desenvolvidas ferramentas para análise do sinal, spike sorting e extração de características por processamento não supervisionado de registos de microeléctrodos humanos. As características relativas ao domínio do tempo e frequência foram obtidas para cada sinal através de medidas estatísticas (média, mediana e desvio padrão) dos registos fracionados em segmentos sobrepostos, para minimizar os efeitos do ruído. As características relacionadas com a atividade neuronal foram também extraídas após deteção e identificação dos diferentes neurónios em cada registo de forma não supervisionada. A localização dos diferentes registos ao longo do trajecto foi realizada por um especialista através de inspeção visual aleatória de todos os sinais. As características extraídas nas regiões do núcleo subtalâmico foram comparadas com as registadas fora de esta área. Posteriormente, uma abordagem híbrida de classificação utilizando métodos de aprendizagem automática -machine learning- foi treinada e testada para identificação dos registos localizados no núcleo subtalâmico utilizando as características extraídas baseadas no domínio do tempo e frequência. Para comparar as sub-regiões funcionais do núcleo subtalâmico, utilizaram-se os sinais identificados em esta região e dividiram-se estas profundidades etiquetadas pelo especialista como núcleo subtalâmico na porção mais dorsal (que terá maior probabilidade de ser motora) e outra ventral (que terá maior probabilidade de ser não motora). Utilizando esta subdivisão, compararamse as características neurofisiológicas relativas às diferentes sub-regiões funcionais com testes estatísticos. Conhecendo as limitações da classificação por inspecção visual, desenvolveu-se uma metodologia para refinamento das localizações dos diferentes registos baseada na reconstrução da trajetória dos eléctrodos implantados, utilizando imagens pre- e pos- operatórias. Esta reconstrução foi sobreposta a um atlas contendo as subdivisões funcionais do núcleo subtalâmico. Os resultados preliminares desta aplicação num sujeito, permitiram mostrar uma classificação mais realista destas sub-regiões na identificação da área motora vs. não motora (límbica e associativa). Resultados Foram identificadas várias características significativamente diferentes no domínio do tempo e frequência, entre os sinais classificados como pertencentes ao Núcleo Subtalámico e os sinais não-pertencentes. Estas diferenças permitiram o desenvolvimento de classificadores do tipo de máquinas de vectores de suporte, utilizando as localizações dos diferentes registos tendo como base a classificação baseada em inspecção visual por um especialista. Utilizando os sinais de maior certeza de classificação, obteve-se uma precisão de classificação do núcleo subtalâmico de 96,18 3,15 % (para 30 trajetórias, 5 pacientes) usando uma abordagem de validação cruzada de “deixar um sujeito fora"com máquina de vectores de suporte linear. Este modelo de validação garante que os dados relativos aos doentes utilizados para treinar o modelo não foram usados posteriormente para testar o mesmo. Utilizando todos os sinais considerados como núcleo subtalâmico independentemente do nível de certeza, obteve-se uma classificação de 84,32 5,75 %. A ferramenta não supervisionada para spike sorting permitiu a identificação de um total de 357 neurónios do núcleo subtalâmico identificado com a máxima certeza em 5 doentes (155 registos de microeléctrodos de 10 segundos de duração) e 521 neurónios foram extraídos de 420 sinais localizados fora do núcleo subtalâmico. Utilizando segmentos de 10 segundos livres de artefactos, identificamos 258 neurónios em 116 sinais do núcleo subtalâmico e 329 neurónios em 228 registos de não-Subtalámico. Foram encontradas diferenças significativas em em 13 das 15 características analisadas comparando STN com não-STN. As subregiões funcionais do núcleo subtalâmico (dorsal vs. ventral) foram comparadas quanto às caracteristicas de tempo, frequência e actividade neuronal. Encontraram-se diferenças significativas em 6 características extraídas dos sinais sem artefactos, consistentes com a literatura e também em uma característica de frequência. Em linha com o descrito previamente, encontramos um burts rate mais elevado na subdivisão dorsal em relação à ventral (mediana (amplitude interquartil) of 1.8(1.5) vs 1.15(0.05) bursts/s, p=0.001) e um firing rate mais alto (mediana (amplitude interquartil) of 21.4(16.85) vs. 15.3(14.33), p=0.013) entre outras características. Os resultados preliminares da análise de sinal refinado por imagem num sujeito, mostraram uma alta accuracy do expert na classificação de sinais STN/não-STN comparados com imagem (>85%). Apesar de neste sujeito não se obterem diferenças significativas entre regiões nas características extraídas, esta analise mostrou que divisão heurística dorsal/ventral era insuficiente para proceder à analise apropriada. Conclusões Foi desenvolvida uma ferramenta para análise de registo de microeléctrodos em humanos e para extração de características no domínio do tempo e frequência, que forneceu bons resultados na classificação do núcleo subtalâmico utilizando a identificação da localização de cada registo por um especialista. A análise e extração de características relativas à atividade neuronal foi realizada por uma ferramenta não supervisionada, que foi desenvolvida combinando algoritmos existentes e que pode ser generalizável a registos em outras regiões ou outro tipo de registos. Esta ferramenta permitiu a identificação de características que permitem diferenciar os sinais colhidos no núcleo subtalâmico dos identificados fora de esta região. De acordo com a literatura, fomos capazes de identificar diferenças em segmentos funcionalmente segregados do núcleo subtalâmico. O trabalho em curso, com refinação anatómica, vai-nos permitir avaliar a sua utilidade em optimizar o posicionamento dos elétrodos, podendo também ser utilizado para fins de investigação.N/

A discrete wavelet transform-based voice activity detection and noise classification with sub-band selection

A real-time discrete wavelet transform-based adaptive voice activity detector and sub-band selection for feature extraction are proposed for noise classification, which can be used in a speech processing pipeline. The voice activity detection and sub-band selection rely on wavelet energy features and the feature extraction process involves the extraction of mel-frequency cepstral coefficients from selected wavelet sub-bands and mean absolute values of all sub-bands. The method combined with a feedforward neural network with two hidden layers could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. In comparison to the conventional short-time Fourier transform-based technique, it has higher F1 scores and classification accuracies (with a mean of 0.916 and 90.1%, respectively) across five different noise types (babble, factory, pink, Volvo (car) and white noise), a significantly smaller feature set with 21 features, reduced memory requirement, faster training convergence and about half the computational cost