Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation.

Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation

Spatio-spectral patterns based on stein kernel for EEG signal classification

El trastorno por déficit de atención con hiperactividad (TDAH) es un trastorno neurológico de inicio en la niñez que puede persistir en la adolescencia y la vida adulta, reduciendo la concentración, la memoria y la productividad. El principal inconveniente de las anomalías de la salud mental de este tipo es la técnica de diagnóstico tradicional, ya que se basa exclusivamente en una descripción sintomatológica sin considerar ningún dato biológico, lo que genera altas tasas de sobrediagnóstico. Para abordar el problema anterior, los investigadores clínicos están intentando extraer biomarcadores de TDAH a partir de señales electroencefalográficas (EEG) registradas. Entre los biomarcadores más comunes se encuentran la relación Theta / Beta y P300, de los cuales estudios recientes han demostrado una falta de importancia en las diferencias entre el TDAH y los sujetos de control. Además, otro gran desafío en el procesamiento del electroencefalograma viene dado por la sensibilidad de las señales, ya que pueden verse fácilmente afectadas por ruidos de fondo, artefactos musculares, movimientos de la cabeza y parpadeos que perjudican enormemente su calidad, lo que limita su introducción en aplicaciones del mundo real. Este trabajo propone una metodología de representación de señales de EEG para identificar discrepancias de respuestas inhibitorias en el sujeto, decodificar la estructura de datos y respaldar el diagnóstico de trastornos mentales. Para esto, primero desarrollamos un enfoque de extracción de características basado en los patrones espaciales comunes (CSP) de las señales de EEG para respaldar el diagnóstico de TDAH como se muestra en el capítulo 3. Luego, desarrollamos una metodología para la representación de señales de EEG que utiliza la similitud entre series de tiempo a través de sus matrices de covarianza en la variedad riemanniana de matrices semidefinitas positivas (PSD), utilizando la divergencia logdet de Jensen Bregman, el kernel de Stein y la alineación de kernel centrada (CKA) como una función de costo para realizar una optimización de filtros espaciales. Finalmente, en el capítulo 5 presentamos una metodología para el apoyo diagnóstico del TDAH. La propuesta implica el uso de los patrones espaciales óptimos desarrollados en el capítulo 4, una descomposición en los ritmos cerebrales y la decodificación discriminativa del capítulo 3. Las características subjetivas resultantes alimentaron un análisis discriminante lineal como herramienta de diagnóstico. La tasa de precisión alcanzada del 93% demuestra que el índice discriminativo basado en los patrones espaciales de stein supera a los biomarcadores convencionales en el diagnóstico de TDAH.Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since this is based exclusively on a symptomatological description without considering any biological data, leading to high overdiagnosis rates. To address the above problem, clinical researchers are attempting to extract ADHD biomarkers from recorded electroencephalographic (EEG) signals. Among the most common biomarkers are Theta/Beta Ratio and P300, of which recent studies have shown a lack of significance on the differences between ADHD and control subjects. Besides, another great challenge in EEG processing is given by the sensitivity of the signals, since they can be easily affected by background noise, muscle artifacts, head movements and flickering that greatly impair their quality, which limits its introduction into real world applications. This work proposes an EEG signal representation methodology for identifying subject-wise discrepancies of inhibitory responses, decoding the data structure, and supporting diagnosis of mental disorders. For this, first we develop a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis as show in chapter 3. Then, we develop a methodology for the representation of EEG signals that uses the similarity between time series through their covariance matrices in the Riemannian manifold of positive semidefinite matrices (PSD), using the logdet-divergence of Jensen Bregman, the Stein kernel, and Centered Kernel Alignment (CKA) as a cost function to perform a spatial filters optimization. Finally, in chapter 5 we present a methodology for the diagnostic support of ADHD. The proposal involves the use of the optimal spatial patterns developed in chapter 4, a decomposition in brain rhythms, and the discriminative decoding of chapter 3. The resulting subject-wise features fed a linear discriminant analysis as the supported-diagnosis tool. Achieved 93% accuracy rate proves that the discriminative index based on the stein spatial patterns outperforms conventional biomarkers in the ADHD diagnosis.MaestríaMagíster en Ingeniería EléctricaContents 1 List of Symbols and Abbreviations 6 1.1 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Abbrevations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Introduction 8 2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . 12 3 CSP-based discriminative capacity index from EEG 13 3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Common Spatial Patterns . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Discriminative decoding of CSP . . . . . . . . . . . . . . . . 14 3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 Synthetic EEG records . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 Real EEG records . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.3 Proposed scheme for feature extraction . . . . . . . . . . . . 19 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.1 Discriminative decoding on simulated data . . . . . . . . . . 19 3.3.2 Feature extraction by discriminative decoding . . . . . . . . . 21 3.3.3 Diagnostic support of ADHD . . . . . . . . . . . . . . . . . 21 4 Multiple Kernel Stein Spatial Patterns 24 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 EEG Decomposition . . . . . . . . . . . . . . . . . . . . . . 24 4.1.2 Time-Series Similarity through the Stein Kernel for PSD Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.3 Spatial Filter Optimization Using Centered Kernel Alignment 27 4.1.4 Assembling of Multiple Kernel Representations . . . . . . . . 27 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2.1 Dataset IIa from BCI Competition IV (BCICIV2a) . . . . . . 28 4.2.2 Proposed BCI Methodology . . . . . . . . . . . . . . . . . . 29 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.3.1 Performance Results . . . . . . . . . . . . . . . . . . . . . . 30 4.3.2 Model Interpretability . . . . . . . . . . . . . . . . . . . . . 33 5 SSP-based discriminative capacity index from EEG supporting ADHD di agnosis 37 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.1 Brain rhythms EEG decomposition . . . . . . . . . . . . . . 38 5.1.2 Stein Spatial Patterns (SSP) . . . . . . . . . . . . . . . . . . 39 5.1.3 Discriminative decoding of SSP . . . . . . . . . . . . . . . . 39 5.1.4 Generative-supervised feature relevance . . . . . . . . . . . . 40 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6 Conclusions 45 6.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Brain electrical activity discriminant analysis using Reproducing Kernel Hilbert spaces

A deep an adequate understanding of the human brain functions has been an objective for interdisciplinar teams of scientists. Different types of technological acquisition methodologies, allow to capture some particular data that is related with brain activity. Commonly, the more used strategies are related with the brain electrical activity, where reflected neuronal interactions are reflected in the scalp and obtained via electrode arrays as time series. The processing of this type of brain electrical activity (BEA) data, poses some challenges that should be addressed carefully due their intrinsic properties. BEA in known to have a nonstationaty behavior and a high degree of variability dependenig of the stimulus or responses that are being adressed..

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

Background: For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods: 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results: The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions: Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation

Toward a model-based predictive controller design in brain-computer interfaces

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS)

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such processes. In that regard, brain connectivity measures constitute a valuable tool in neuroscience. They allow assessing functional interactions among brain regions through directed or non-directed statistical dependencies estimated from neural time series. Transfer entropy (TE) is one such measure. It is an effective connectivity estimation approach based on information theory concepts and statistical causality premises. It has gained increasing attention in the literature because it can capture purely nonlinear directed interactions, and is model free. That is to say, it does not require an initial hypothesis about the interactions present in the data. These properties make it an especially convenient tool in exploratory analyses. However, like any information-theoretic quantity, TE is defined in terms of probability distributions that in practice need to be estimated from data. A challenging task, whose outcome can significantly affect the results of TE. Also, it lacks a standard spectral representation, so it cannot reveal the local frequency band characteristics of the interactions it detects.Las interacciones entre poblaciones neuronales distribuidas en diferentes regiones del cerebro son el núcleo del procesamiento cognitivo y perceptivo. Por lo tanto, la capacidad de estudiar el flujo de información dentro de redes de conjuntos neuronales conectados es de fundamental importancia para comprender dichos procesos. En ese sentido, las medidas de conectividad cerebral constituyen una valiosa herramienta en neurociencia. Permiten evaluar interacciones funcionales entre regiones cerebrales a través de dependencias estadísticas dirigidas o no dirigidas estimadas a partir de series de tiempo. La transferencia de entropía (TE) es una de esas medidas. Es un enfoque de estimación de conectividad efectiva basada en conceptos de teoría de la información y premisas de causalidad estadística. Ha ganado una atención cada vez mayor en la literatura porque puede capturar interacciones dirigidas puramente no lineales y no depende de un modelo. Es decir, no requiere de una hipótesis inicial sobre las interacciones presentes en los datos. Estas propiedades la convierten en una herramienta especialmente conveniente en análisis exploratorios. Sin embargo, como cualquier concepto basado en teoría de la información, la TE se define en términos de distribuciones de probabilidad que en la práctica deben estimarse a partir de datos. Una tarea desafiante, cuyo resultado puede afectar significativamente los resultados de la TE. Además, carece de una representación espectral estándar, por lo que no puede revelar las características de banda de frecuencia local de las interacciones que detecta.DoctoradoDoctor(a) en IngenieríaContents List of Figures xi List of Tables xv Notation xvi 1 Preliminaries 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Probability distribution estimation as an intermediate step in TE computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 The lack of a spectral representation for TE . . . . . . . . . . . . 7 1.3 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.2 Granger causality . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Information theoretic learning from kernel matrices . . . . . . . . 12 1.4 Literature review on transfer entropy estimation . . . . . . . . . . . . . . 14 1.4.1 Transfer entropy in the frequency domain . . . . . . . . . . . . . . 17 1.5 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.1 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.2 Specific aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.6 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . 24 1.6.2 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . 24 1.6.3 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . 25 1.7 EEG databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Contents ix 1.7.1 Motor imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.7.2 Working memory . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2 Kernel-based Renyi’s transfer entropy 34 2.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . . . . . 35 2.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 38 2.2.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.4 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 46 2.3.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3 Kernel-based Renyi’s phase transfer entropy 60 3.1 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . . . . . 61 3.1.1 Phase-based effective connectivity estimation approaches considered in this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions 84 4.1 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . . . . . . . . . . 85 x Contents 4.1.1 Transfer entropy for directed phase-amplitude interactions . . . . 85 4.1.2 Cross-frequency directionality . . . . . . . . . . . . . . . . . . . . 85 4.1.3 Phase transfer entropy and directed phase-amplitude interactions 86 4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 88 4.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.3.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 92 4.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Final Remarks 100 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 Academic products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 Journal papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2 Conference papers . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.3.3 Conference presentations . . . . . . . . . . . . . . . . . . . . . . . 105 Appendix A Kernel methods and Renyi’s entropy estimation 106 A.1 Reproducing kernel Hilbert spaces . . . . . . . . . . . . . . . . . . . . . . 106 A.1.1 Reproducing kernels . . . . . . . . . . . . . . . . . . . . . . . . . 106 A.1.2 Kernel-based learning . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.2 Kernel-based estimation of Renyi’s entropy . . . . . . . . . . . . . . . . . 109 Appendix B Surface Laplacian 113 Appendix C Permutation testing 115 Appendix D Kernel-based relevance analysis 117 Appendix E Cao’s criterion 120 Appendix F Neural mass model equations 122 References 12