Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

Robust artifactual independent component classification for BCI practitioners

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, ZentrumDFG, 194657344, EXC 1086: BrainLinks-BrainTool

Generalized Sparse Discriminant Analysis for Event-Related Potential Classification

A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called generalized sparse discriminant analysis (GSDA), for binary classification. This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The GSDA method is designed to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that, on one hand, GSDA outperforms standard SDA in the sense of classification performance, sparsity and required computing time, and, on the other hand, it also yields better overall performances, compared to well-known ERP classification algorithms, for single-trial ERP classification when insufficient training samples are available. Hence, GSDA constitute a potential useful method for reducing the calibration times in ERP-based BCI systems.Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentin

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

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

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

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..