A New Regularized Matrix Discriminant Analysis (R-MDA) Enabled Human-Centered EEG Monitoring Systems

The wider use of wearable devices for electroencephalogram (EEG) data capturing providesa very useful way for the monitoring and self-management of human health. However, the large volumesof data with high dimensions cause computational complexity in EEG data processing and pose a greatchallenge to the use of wearable EEG devices in healthcare. This paper proposes a new approach to extract thestructural information of EEG data and tackle the curse of dimensionality of the EEG data. A set of methodsfor dimensionality reduction (DR)-like linear discriminant analysis (LDA) and their improved methodshave been developed for EEG processing in the literature. However, the existing LDA-related methodssuffer from the singularity problem or expensive computational cost, and none of existing methods takeinto consideration the structure of the projection matrix, which is crucial for the extraction of the structuralinformation of the EEG data. In this paper, a new method called a regularized matrix discriminant analysis(R-MDA) is proposed for EEG feature representation and DR. In the R-MDA, the EEG data are representedas a data matrix, and projection vectors are reshaped to be a set of projection matrices stacking together. Byreformulating the LDA as a least-square formulation and imposing specified constraint on each projectionmatrix, the new R-MDA has been constructed to effectively reduce EEG dimensions and capturing thestructural information of the EEG data. Experimental results demonstrate that this new R-MDA outperformsthe existing LDA-related methods, including achieving improved accuracy with significant DR of the EEGdata. This offers an effective way to enable wearable EEG devices be applicable in human-centered healthmonitorin

FINDING EEG SPACE-TIME-SCALE LOCALIZED FEATURES USING MATRIX-BASED PENALIZED DISCRIMINANT ANALYSIS

International audienceThis paper proposes a new method for constructing and selecting of discriminant space-time-scale features for electroencephalogram (EEG) signal classification, suitable for Error Related Potentials (ErrP)detection in brain-computer interface (BCI). The method rests on a new variant of matrix-variate Linear Discriminant Analysis (LDA), and differs from previously proposed approaches in mainly three ways. First, a discrete wavelet expansion is introduced for mapping time-courses to time-scale coefficients, yielding time-scale localized features. Second, the matrix-variate LDA is modified in such a way that it yields an interesting duality property, that makes interpretation easier. Third, a space penalization is introduced using a surface Laplacian, so as to enforce spatial smoothness. The proposed approaches, termed D-MLDA and D-MPDA are tested on EEG signals, with the goal of detecting ErrP. Numerical results show that D-MPDA outperforms D-MLDA and other matrix-variate LDA techniques. In addition this method produces relevant features for interpretation in ErrP signals

Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems

Brain-Computer Interface (BCI) system provides a channel for the brain to control external devices using electrical activities of the brain without using the peripheral nervous system. These BCI systems are being used in various medical applications, for example controlling a wheelchair and neuroprosthesis devices for the disabled, thereby assisting them in activities of daily living. People suffering from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked in are unable to perform any body movements because of the damage of the peripheral nervous system, but their cognitive function is still intact. BCIs operate external devices by acquiring brain signals and converting them to control commands to operate external devices. Motor-imagery (MI) based BCI systems, in particular, are based on the sensory-motor rhythms which are generated by the imagination of body limbs. These signals can be decoded as control commands in BCI application. Electroencephalogram (EEG) is commonly used for BCI applications because it is non-invasive. The main challenges of decoding the EEG signal are because it is non-stationary and has a low spatial resolution. The common spatial pattern algorithm is considered to be the most effective technique for discrimination of spatial filter but is easily affected by the presence of outliers. Therefore, a robust algorithm is required for extraction of discriminative features from the motor imagery EEG signals. This thesis mainly aims in developing robust spatial filtering criteria which are effective for classification of MI movements. We have proposed two approaches for the robust classification of MI movements. The first approach is for the classification of multiclass MI movements based on the thinICA (Independent Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method. The observed results indicate that these approaches can be a step towards the development of robust feature extraction for MI-based BCI system. The main contribution of the thesis is the second criterion, which is based on Alpha- Beta logarithmic-determinant divergence for the classification of two class MI movements. A detailed study has been done by obtaining a link between the AB log det divergence and CSP criterion. We propose a scaling parameter to enable a similar way for selecting the respective filters like the CSP algorithm. Additionally, the optimization of the gradient of AB log-det divergence for this application was also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence) algorithm is proposed for the discrimination of two class MI movements. The robustness of this algorithm is tested with both the simulated and real data from BCI competition dataset. Finally, the resulting performances of the proposed algorithms have been favorably compared with other existing algorithms

Unsupervised Common Spatial Patterns

The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.Ministerio de Economía y Competitividad (España) TEC2017-82807-

EEG Signal Processing in Motor Imagery Brain Computer Interfaces with Improved Covariance Estimators

Desde hace unos años hasta la actualidad, el desarrollo en el campo de los interfaces cerebro ordenador ha ido aumentando. Este aumento viene motivado por una serie de factores distintos. A medida que aumenta el conocimiento acerca del cerebro humano y como funciona (del que aún se conoce relativamente poco), van surgiendo nuevos avances en los sistemas BCI que, a su vez, sirven de motivación para que se investigue más acerca de este órgano. Además, los sistemas BCI abren una puerta para que cualquier persona pueda interactuar con su entorno independientemente de la discapacidad física que pueda tener, simplemente haciendo uso de sus pensamientos. Recientemente, la industria tecnológica ha comenzado a mostrar su interés por estos sistemas, motivados tanto por los avances con respecto a lo que conocemos del cerebro y como funciona, como por el uso constante que hacemos de la tecnología en la actuali- dad, ya sea a través de nuestros smartphones, tablets u ordenadores, entre otros muchos dispositivos. Esto motiva que compañías como Facebook inviertan en el desarrollo de sistemas BCI para que tanto personas sin discapacidad como aquellas que, si las tienen, puedan comunicarse con los móviles usando solo el cerebro. El trabajo desarrollado en esta tesis se centra en los sistemas BCI basados en movimien- tos imaginarios. Esto significa que el usuario piensa en movimientos motores que son interpretados por un ordenador como comandos. Las señales cerebrales necesarias para traducir posteriormente a comandos se obtienen mediante un equipo de EEG que se coloca sobre el cuero cabelludo y que mide la actividad electromagnética producida por el cere- bro. Trabajar con estas señales resulta complejo ya que son no estacionarias y, además, suelen estar muy contaminadas por ruido o artefactos. Hemos abordado esta temática desde el punto de vista del procesado estadístico de la señal y mediante algoritmos de aprendizaje máquina. Para ello se ha descompuesto el sistema BCI en tres bloques: preprocesado de la señal, extracción de características y clasificación. Tras revisar el estado del arte de estos bloques, se ha resumido y adjun- tado un conjunto de publicaciones que hemos realizado durante los últimos años, y en las cuales podemos encontrar las diferentes aportaciones que, desde nuestro punto de vista, mejoran cada uno de los bloques anteriormente mencionados. De manera muy resumida, para el bloque de preprocesado proponemos un método mediante el cual conseguimos nor- malizar las fuentes de las señales de EEG. Al igualar las fuentes efectivas conseguimos mejorar la estima de las matrices de covarianza. Con respecto al bloque de extracción de características, hemos conseguido extender el algoritmo CSP a casos no supervisados. Por último, en el bloque de clasificación también hemos conseguido realizar una sepa- ración de clases de manera no supervisada y, por otro lado, hemos observado una mejora cuando se regulariza el algoritmo LDA mediante un método específico para Gaussianas.The research and development in the field of Brain Computer Interfaces (BCI) has been growing during the last years, motivated by several factors. As the knowledge about how the human brain is and works (of which we still know very little) grows, new advances in BCI systems are emerging that, in turn, serve as motivation to do more re- search about this organ. In addition, BCI systems open a door for anyone to interact with their environment regardless of the physical disabilities they may have, by simply using their thoughts. Recently, the technology industry has begun to show its interest in these systems, mo- tivated both by the advances about what we know of the brain and how it works, and by the constant use we make of technology nowadays, whether it is by using our smart- phones, tablets or computers, among many other devices. This motivates companies like Facebook to invest in the development of BCI systems so that people (with or without disabilities) can communicate with their devices using only their brain. The work developed in this thesis focuses on BCI systems based on motor imagery movements. This means that the user thinks of certain motor movements that are in- terpreted by a computer as commands. The brain signals that we need to translate to commands are obtained by an EEG device that is placed on the scalp and measures the electromagnetic activity produced by the brain. Working with these signals is complex since they are non-stationary and, in addition, they are usually heavily contaminated by noise or artifacts. We have approached this subject from the point of view of statistical signal processing and through machine learning algorithms. For this, the BCI system has been split into three blocks: preprocessing, feature extraction and classification. After reviewing the state of the art of these blocks, a set of publications that we have made in recent years has been summarized and attached. In these publications we can find the different contribu- tions that, from our point of view, improve each one of the blocks previously mentioned. As a brief summary, for the preprocessing block we propose a method that lets us nor- malize the sources of the EEG signals. By equalizing the effective sources, we are able to improve the estimation of the covariance matrices. For the feature extraction block, we have managed to extend the CSP algorithm for unsupervised cases. Finally, in the classification block we have also managed to perform a separation of classes in an blind way and we have also observed an improvement when the LDA algorithm is regularized by a specific method for Gaussian distributions

Study of Adaptation Methods Towards Advanced Brain-computer Interfaces

Ph.DDOCTOR OF PHILOSOPH

High Dimensional Covariance Estimation for Spatio-Temporal Processes

High dimensional time series and array-valued data are ubiquitous in signal processing, machine learning, and science. Due to the additional (temporal) direction, the total dimensionality of the data is often extremely high, requiring large numbers of training examples to learn the distribution using unstructured techniques. However, due to difficulties in sampling, small population sizes, and/or rapid system changes in time, it is often the case that very few relevant training samples are available, necessitating the imposition of structure on the data if learning is to be done. The mean and covariance are useful tools to describe high dimensional distributions because (via the Gaussian likelihood function) they are a data-efficient way to describe a general multivariate distribution, and allow for simple inference, prediction, and regression via classical techniques. In this work, we develop various forms of multidimensional covariance structure that explicitly exploit the array structure of the data, in a way analogous to the widely used low rank modeling of the mean. This allows dramatic reductions in the number of training samples required, in some cases to a single training sample. Covariance models of this form have been increasing in interest recently, and statistical performance bounds for high dimensional estimation in sample-starved scenarios are of great relevance. This thesis focuses on the high-dimensional covariance estimation problem, exploiting spatio-temporal structure to reduce sample complexity. Contributions are made in the following areas: (1) development of a variety of rich Kronecker product-based covariance models allowing the exploitation of spatio-temporal and other structure with applications to sample-starved real data problems, (2) strong performance bounds for high-dimensional estimation of covariances under each model, and (3) a strongly adaptive online method for estimating changing optimal low-dimensional metrics (inverse covariances) for high-dimensional data from a series of similarity labels.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137082/1/greenewk_1.pd

A machine learning approach to taking EEG-based brain-computer interfaces out of the lab

Despite being a subject of study for almost three decades, non-invasive brain- computer interfaces (BCIs) are still trapped in the laboratory. In order to move into more common use, it is necessary to have systems that can be reliably used over time with a minimum of retraining. My research focuses on machine learning methods to minimize necessary retraining, as well as a data science approach to validate processing pipelines more robustly. Via a probabilistic transfer learning method that scales well to large amounts of data in high dimensions it is possible to reduce the amount of calibration data needed for optimal performance. However, a good model still requires reliable features that are resistant to recording artifacts. To this end we have also investigated a novel feature of the electroencephalogram which is predictive of multiple types of brain-related activity. As cognitive neuroscience literature suggests, shifts in the peak frequency of a neural oscillation – hereafter referred to as frequency modulation – can be predictive of activity in standard BCI tasks, which we validate for the first time in multiple paradigms. Finally, in order to test the robustness of our techniques, we have built a codebase for reliable comparison of pipelines across over fifteen open access EEG datasets

Cortically coupled image computing

In the 1970s, researchers at the University of California started to investigate communication between humans and computers using neural signals, which lead to the emergence of brain- computer interfaces (BCIs). In the past 40 years, significant progress has been achieved in ap- plication areas such as neuroprosthetics and rehabilitation. BCIs have been recently applied to media analytics (e.g., image search and information retrieval) as we are surrounded by tremen- dous amounts of media information today. A cortically coupled computer vision (CCCV) sys- tem is a type of BCI that exposes users to high throughput image streams via the rapid serial visual presentation (RSVP) protocol. Media analytics has also been transformed through the enormous advances in artificial intelligence (AI) in recent times. Understanding and presenting the nature of the human-AI relationship will play an important role in our society in the future. This thesis explores two lines of research in the context of traditional BCIs and AI. Firstly, we study and investigate the fundamental processing methods such as feature extraction and clas- sification for CCCV systems. Secondly, we discuss the feasibility of interfacing neural systems with AI technology through CCCV, an area we identify as neuro-AI interfacing. We have made two electroencephalography (EEG) datasets available to the community that support our inves- tigation of these two research directions. These are the neurally augmented image labelling strategies (NAILS) dataset and the neural indices for face perception analysis (NIFPA) dataset, which are introduced in Chapter 2. The first line of research focuses on studying and investigating fundamental processing methods for CCCV. In Chapter 3, we present a review on recent developments in processing methods for CCCV. This review introduces CCCV related components, specifically the RSVP experimental setup, RSVP-EEG phenomena such as the P300 and N170, evaluation metrics, feature extraction and classification. We then provide a detailed study and an analysis on spatial filtering pipelines in Chapter 4, which are the most widely used feature extraction and reduction methods in a CCCV system. In this context, we propose a spatial filtering technique named multiple time window LDA beamformers (MTWLB) and compare it to two other well-known techniques in the literature, namely xDAWN and common spatial patterns (CSP). Importantly, we demonstrate the efficacy of MTWLB for time-course source signal reconstruction compared to existing methods, which we then use as a source signal information extraction method to support a neuro-AI interface. This will be further discussed in this thesis i.e. Chapter 6 and Chapter 7. The latter part of this thesis investigates the feasibility of neuro-AI interfaces. We present two research studies which contribute to this direction. Firstly, we explore the idea of neuro- AI interfaces based on stimulus and neural systems i.e., observation of the effects of stimuli produced by different AI systems on neural signals. We use generative adversarial networks (GANs) to produce image stimuli in this case as GANs are able to produce higher quality images compared to other deep generative models. Chapter 5 provides a review on GAN-variants in terms of loss functions and architectures. In Chapter 6, we design a comprehensive experiment to verify the effects of images produced by different GANs on participants’ EEG responses. In this we propose a biologically-produced metric called Neuroscore for evaluating GAN per- formance. We highlight the consistency between Neuroscore and human perceptual judgment, which is superior to conventional metrics (i.e., Inception Score (IS), Fre ́chet Inception Distance (FID) and Kernel Maximum Mean Discrepancy (MMD) discussed in this thesis). Secondly, in order to generalize Neuroscore, we explore the use of a neuro-AI interface to help convolutional neural networks (CNNs) predict a Neuroscore with only an image as the input. In this scenario, we feed the reconstructed P300 source signals to the intermediate layer as supervisory informa- tion. We demonstrate that including biological neural information can improve the prediction performance for our proposed CNN models and the predicted Neuroscore is highly correlated with the real Neuroscore (as directly calculated from human neural signals)