Spike Train Discrimination and Analysis in Neural and Surface Electromyography (sEMG) Applications (Het onderscheiden en analyseren van spike-treinen in neurale en oppervlakte electromyogram (sEMG) toepassingen)

Acknowledgments iii Abstract vi Samenvatting viii Abbreviations xi Contents 1 Overview of the thesis 5 1 Introduction on spike sorting 9 1.1 Spike sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Origin and recording of neural spikes . . . . . . . . . . . . . . . . . 10 1.3 Signal filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4 Spike detection and alignment . . . . . . . . . . . . . . . . . . . . . 19 1.4.1 Spike detection . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.4.2 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Spike classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.5.1 Feature extraction . . . . . . . . . . . . . . . . . . . . . . . 23 1.5.2 Feature reduction and classification . . . . . . . . . . . . . . 27 1 2 CONTENTS 1.5.3 Variability of the spike shape . . . . . . . . . . . . . . . . . 30 1.6 Superparamagnetic clustering algorithm . . . . . . . . . . . . . . . 31 1.6.1 Outline of the algorithm . . . . . . . . . . . . . . . . . . . . 31 1.6.2 Variables of the model . . . . . . . . . . . . . . . . . . . . . 32 1.6.3 Iterative procedure and achieving the clustering . . . . . . . 33 1.6.4 Influence and setting of parameters . . . . . . . . . . . . . . 34 2 Reliability of statistical features describing neural spike trains in the presence of classification errors 37 2.1 Firing rate function of a spike train . . . . . . . . . . . . . . . . . . 37 2.2 Neuronal coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.1 Rate and temporal coding . . . . . . . . . . . . . . . . . . . 39 2.2.2 What can we infer from the spike-rate function? . . . . . . 42 2.2.3 Homogeneous and Inhomogeneous Poisson process . . . . . 42 2.3 Inter-spike interval (ISI) and autocorrelation histograms . . . . . . 44 2.4 Reliability of statistical features describing neural spike trains in the presence of classification errors . . . . . . . . . . . . . . . . . . . . 47 2.4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.5 Additional comments on chapter composition . . . . . . . . . . . . 61 3 Obsessive-compulsive disorder (OCD) study 63 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.2.1 Subjects and housing . . . . . . . . . . . . . . . . . . . . . . 65 3.2.2 Schedule-induced polydipsia model and experimental groups 66 3.2.3 Electrophysiological recording . . . . . . . . . . . . . . . . . 66 CONTENTS 3 3.2.4 Histology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.2.5 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.2.6 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . 68 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Firing rate and firing pattern in SIP rats, resistant rats and control rats . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.3.2 Left-right hemisphere comparisons . . . . . . . . . . . . . . 72 3.3.3 Correlations with position of the neurons within the BST . 75 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 3.4.1 Neuronal activity in the BST . . . . . . . . . . . . . . . . . 75 3.4.2 The BST in relation to OCD . . . . . . . . . . . . . . . . . 77 3.4.3 General considerations . . . . . . . . . . . . . . . . . . . . . 78 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4 Surface electromyography (sEMG) 81 4.1 Human muscle physiology . . . . . . . . . . . . . . . . . . . . . . . 81 4.2 Measuring electrical activity of the muscle . . . . . . . . . . . . . . 84 4.3 Decomposition of HD-sEMG . . . . . . . . . . . . . . . . . . . . . 87 4.4 Applications of surface EMG . . . . . . . . . . . . . . . . . . . . . 91 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5 A new and fast approach towards HDsEMG decomposition 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.2.3 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.3.1 Simulated data . . . . . . . . . . . . . . . . . . . . . . . . . 110 4 CONTENTS 5.3.2 Real data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.4.1 Simulated data . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.4.2 Real data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6 Correcting electrode displacement errors in motor unit tracking using high density surface electromyography (HDsEMG) 123 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6.2.1 Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6.2.2 Artificial recording grid displacement . . . . . . . . . . . . . 127 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7 Conclusion and Future Work 139 7.1 Analysis of neuronal signals using multichannel neuroprobes . . . . 139 7.1.1 Automated analysis of neural spike trains . . . . . . . . . . 139 7.1.2 Future work on neural spike train processing . . . . . . . . 141 7.2 Utilizing HD-sEMG for the analysis of muscle signals . . . . . . . . 143 7.2.1 Analysis of signals recorded using HD-sEMG . . . . . . . . 143 7.2.2 Future work on HD-sEMG . . . . . . . . . . . . . . . . . . 144 Bibliography 149 List of Publications 165 Curriculum Vitae 185nrpages: 202status: publishe

Signal analysis of the effects of microlesions in the medial septal area on the dominant hippocampal rhythm in anesthetized rats

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Benefits of instructed responding in manual assembly tasks: An ERP approach

© 2016 Mijović, Ković, De Vos, Mačužić, Jeremić and Gligorijević. The majority of neuroergonomics studies are focused mainly on investigating the interaction between operators and automated systems. Far less attention has been dedicated to the investigation of brain processes in more traditional workplaces, such as manual assembly, which are still ubiquitous in industry. The present study investigates whether assembly workers’ attention can be enhanced if they are instructed with which hand to initiate the assembly operation, as opposed to the case when they can commence the operation with whichever hand they prefer. For this aim, we replicated a specific workplace, where 17 participants in the study simulated a manual assembly operation of the rubber hoses that are used in vehicle hydraulic brake systems, while wearing wireless electroencephalography (EEG). The specific EEG feature of interest for this study was the P300 components’ amplitude of the event-related potential (ERP), as it has previously been shown that it is positively related to human attention. The behavioral attention-related modality of reaction times (RTs) was also recorded. Participants were presented with two distinct tasks during the simulated operation, which were counterbalanced across participants. In the first task, digits were used as indicators for the operation initiation (Numbers task), where participants could freely choose with which hand they would commence the action upon seeing the digit. In the second task, participants were presented with arrows, which served as instructed operation initiators (Arrows task), and they were instructed to start each operation with the hand that corresponded to the arrow direction. The results of this study showed that the P300 amplitude was significantly higher in the instructed condition. Interestingly, the RTs did not differ across any task conditions. This, together with the other findings of this study, suggests that attention levels can be increased using instructed responses without compromising work performance or operators’ well-being, paving the way for future applications in manual assembly task design

A novel approach to voluntary HD-sEMG decomposition

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A Multichannel Integrated Circuit for Electrical Recording of Neural Activity, With Independent Channel Programmability

Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1–6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 ${rm nV}/sqrt {rm Hz}$, a NEF of 5.1, a ${rm CMRR} > 60~{rm dB}$, a ${rm THD} < 1$ and a sampling rate of 30 kS/s per channel, while consuming a maximum of 70 $mu {rm A}$ per channel from a single 3.3 V. The ASIC was implemented in a 0.35 $mu {rm m}$ CMOS technology and has a total area of 5.6$,times ,$4.5 ${rm mm}^{2}$. The recording system was successfully validated in in vitro and in vivo experiments, achieving simultaneous multichannel recordings of cell activity with satisfactory signal-to-noise ratios.status: publishe

Motor Unit Tracking Using High Density Surface Electromyography (HDsEMG) Automated Correction of Electrode Displacement Errors

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Ad-vanced Methods for Neural Signals and Images". OBJECTIVES: The study discusses a technique to automatically correct for effects of electrode grid displacement across serial surface EMG measurements with high-density electrode arrays (HDsEMG). The goal is to match motor unit signatures from subsequent measurements and by this, achieve automated motor unit tracking. METHODS: Test recordings of voluntary muscle contractions using HDsEMG were performed on three healthy individuals. Electrode grid displacements were mimicked in repeated recordings while measuring the exact position of the grid. A concept of accounting for translational and rotational displacements by making the projection of the recorded motor unit action potentials is first introduced. Then, this concept was tested for the performed measurements attempting the automated matching of the similar motor unit action potentials across different trials. RESULTS: The ability to perform automated correction (projection) of the isolated motor unit action potentials was first shown using large angular displacements. Then, for accidental (small) displacements of the recording grid, the ability to automatically track motor units across different measurement trials was shown. It was possible to track 10 -15% of identified motor units. CONCLUSIONS: This proof of concept study demonstrates an automated correction allowing the identification of an increased number of same motor unit action potentials across different measurements. By this, great potential is demonstrated for assisting motor unit tracking studies, indicating that otherwise electrode displacements cannot always be precisely described.status: publishe