Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time-annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks

Visualization and exploration of multichannel EEG coherence networks

The brain is the most complicated organ of our body. Modern imaging techniques provide a way to help us to understand mechanisms of brain function underlying human behaviour. One direction of studying these data is to analyze synchrony properties among activities from different brain areas under various conditions. Electroencephalography (EEG) is a technique which is used to measure electric brain potentials under certain conditions. An EEG coherence network may then be constructed based on the obtained EEG signals, where coherence is a measure of the degree of synchrony between EEG signals. However, at the start of a scientific investigation, we usually do not know what kind of information (features) about the data can be useful for further study, and in that case the existing analytical methods are not suitable for the data at hand. For these cases, first visually exploring all the available data could give us an impression of striking patterns or deviations in the data. These observations can then help researchers to propose detailed hypotheses about the data. However, due to the complexity of the data at hand, most existing visualization methods used for a particular task or situation cannot be easily generalized to other cases. Therefore, the visual data exploration should include the context of the visualized structures and take into account requirements from domain experts. Based on this, this thesis provides a number of visualization methods to help researchers analyze both static and dynamic EEG coherence networks

Supported diagnosis of adhd from eeg signals based on hidden markov models and probability product kernels

Attention deficit hyperactivity disorder (ADHD), most often present in childhood, may persist in adult life, hampering personal development. However, ADHD diagnosis is a real challenge since it highly depends on the clinical observation of the patient, the parental and scholar information, and the specialist expertise. Despite demanded objective diagnosis aids from biosignals, the physiological biomarkers lack robustness and significance under the non-stationary and non-linear electroencephalographic dynamics. Therefore, this work presents a supported diagnosis methodology for ADHD from the dynamic characterization of EEG based on hidden Markov models (HMM) and probability product kernels (PPK). Based on the symptom of impulsivity, the proposed approach trains an HMM for each subject from EEG signals in failed inhibition tasks. In the first instance, PPK measures the similarity between subjects through the inner product between their trained HMMs. Then, given the computational costs, fast computation of PPK for HMM facilitates parameter tuning of kernel similarity. Finally, the Kernel Principal Component Analysis (KPCA) projects the PPK to a lower dimensional space, allowing the interpretability of the results. Thus, a support vector machine supports the diagnosis of ADHD as a classification task using PPK as the inner product operator. The methodology compared classification results on EEG signals with all channels, channels of interest (COI), and analysis in the Theta, Alpha, and Beta frequency bands. The results show an accuracy rate of 97.0% in the Beta band in COI, which supports the assumption that this frequency rhythm may be correlated to differences between ADHD and controls regarding attentional allocation during the execution of the cognitive task.El trastorno por déficit de atención e hiperactividad (TDAH), que suele presentarse en la infancia, puede persistir en la vida adulta, obstaculizando el desarrollo personal. Sin embargo, el diagnóstico del TDAH es un verdadero reto, ya que depende en gran medida de la observación clínica del paciente, de la información de los padres y de los estudiosos, y de la experiencia de los especialistas. A pesar de la demanda de ayudas para el diagnóstico objetivo a partir de bioseñales, los biomarcadores fisiológicos carecen de robustez y significación bajo la dinámica electroencefalográfica no estacionaria y no lineal. Por lo tanto, este trabajo presenta una metodología de diagnóstico apoyada para el TDAH a partir de la caracterización dinámica del EEG basada en modelos ocultos de Markov (HMM) y productos de kernel de probabilidad (PPK). Basándose en el síntoma de impulsividad, el enfoque propuesto entrena un HMM para cada sujeto a partir de las señales del EEG en tareas de inhibición fallidas. En primer lugar, el PPK mide la similitud entre los sujetos a través del producto interno entre sus HMMs entrenados. Luego, dados los costes computacionales, el cálculo rápido de PPK para los HMM facilita el ajuste de los parámetros de similitud del kernel. Por último, el Análisis de Componentes Principales del Kernel (KPCA) proyecta el PPK a un espacio de menor dimensión, lo que permite la interpretabilidad de los resultados. Así, una máquina de vectores de apoyo apoya el diagnóstico del TDAH como una tarea de clasificación utilizando el PPK como operador de producto interno. La metodología comparó los resultados de clasificación en señales de EEG con todos los canales, canales de interés (COI), y análisis en las bandas de frecuencia Theta, Alpha, y Beta. Los resultados muestran una tasa de precisión del 97,0% en la banda Beta en COI, lo que apoya la suposición de que este ritmo de frecuencia puede estar correlacionado con las diferencias entre el TDAH y los controles en cuanto a la asignación atencional durante la ejecución de la tarea cognitiva.MaestríaMagíster en Ingeniería EléctricaContents 1 List of Symbols and Abbreviations 5 1.1 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Abbrevations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Introduction 7 2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Develop a multichannel time series classification methodology taking into account signal dynamics 13 3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Similarity between time series . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 EEG Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 HMM training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.3 Parameter tuning and Classification . . . . . . . . . . . . . . . . . . . 17 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4 Develop a time series classification methodology that takes into account spectral information and reduces the computational cost of training. 21 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.1 Fast computation of PPK for HMM . . . . . . . . . . . . . . . . . . . 22 4.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.1 Synthetic Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2.2 Training and Parameter tuning and classification . . . . . . . . . . . 24 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1 CONTENTS 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5 Develop a methodology for visualizing stochastic representations to facilitate the interpretability of inference machines 32 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1.1 Model interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1.2 Low-dimensional HMM visualization . . . . . . . . . . . . . . . . . . 33 5.1.3 Low-dimensional state visualization . . . . . . . . . . . . . . . . . . 34 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 6 Conclusions 4

Connectivity Analysis of Electroencephalograms in Epilepsy

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness

Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data

SaS-BCI: A New Strategy to Predict Image Memorability and use Mental Imagery as a Brain-Based Biometric Authentication

Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication

EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments