Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the “leave-one-out cross-validation” scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors’ correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers’ signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem. Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers. The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient’s EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM. Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively

Dissociation Between Attention and Consciousness During a Novel Task: an ERP Study

While consciousness and attention seem to be tightly connected, recent evidence has suggested that one of these processes can be present in the absence of the other. Recent researches showed that observers can pay attention to an invisible (unconscious) stimulus, and that a stimulus can be clearly perceived (seen) in the absence of attention. We have proposed a novel psychophysical task to explore neural correlates of top-down attention and consciousness. The task is meant to confirm that these two processes can occur independently of each other. EEG was recorded during realizations of the task and target-locked event-related potentials (ERPs) for masked and unmasked conditions were constructed. Time features corresponding to the P100, N150, and P300 components were extracted for each channel separately. Utilizing these features, we employed some common classifiers for classification of the fourfold state. Our task could separate attention and consciousness successfully through their neural correlates. The results indicate that some of the mentioned components changed when attention or consciousness occurs. By comparing difference waves in each condition separately, our results introduce new ERP correlates of attention and consciousness. We also revealed that parieto-occipital areas are the most relevant areas for dissociation between attention and consciousness. To our knowledge, this is the first time that these correlates are introduced in a separable mode, and that the classification accuracies are reported for this purpose.Як вважають, увага та усвідомлення тісно поєднані, проте результати недавніх досліджень дають підстави думати, що один із цих процесів може реалізуватися за відсутності другого. У новітніх експериментах виявилося, що спостерігачі можуть приділяти увагу невидимому (неусвідомлюваному) стимулу і що стимул може чітко розпізнаватися (диференціюватися) за відсутності уваги. Ми запропонували нове психофізіологічне завдання для дослідження нервових корелятів змін рівнів уваги та усвідомлення. Цільові ЕЕГпотенціали, пов’язані з подією (ППП), відводили в умовах пред’явлення замаскованих та незамаскованих візуальних патернів. Часові характеристики компонентів P100, N150 та P300 визначалися роздільно для відведень по кожному ЕЕГканалу. З урахуванням цих характеристик були використані певні загальні класифікатори для параметрів, спостережуваних у всіх чотирьох стимуляційних станах. У нашому тесті ефекти уваги та усвідомлення могли бути успішно розділені відповідно до їх нервових ЕЕГ-корелятів. Згідно з отриманими результатами, згадані вище компоненти ППП змінюються залежно від того, як проявляється увага або усвідомлення. Роздільне порівняння відмінностей між хвилями ППП для кожної з умов дозволило виявити нові кореляти уваги та усвідомлення, що відбиваються в ППП. Показано також, що тім’яно-потиличні кортикальні зони є структурами, найбільшою мірою пов’язаними з дисоціацією ефектів уваги та усвідомлення. Як ми вважаємо, ці кореляти вперше представлені з використанням методики, котра дозволяє їх розрізнити, і наведені дані щодо точності такої диференціації

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Facial EMG – Investigating the Interplay of Facial Muscles and Emotions

This chapter provides information about facial electromyography (EMG) as a method of investigating emotions and affect, including examples of application and methods for analysis. This chapter begins with a short introduction to emotion theory followed by an operationalisation of facial emotional expressions as an underlying requirement for their study using facial EMG. This chapter ends by providing practical information on the use of facial EMG

Social and Affective Neuroscience of Embodiment

Embodiment has been discussed in the context of social, affective, and cognitive psychology, and also in the investigations of neuroscience in order to understand the relationship between biological mechanisms, body and cognitive, and social and affective processes. New theoretical models have been presented by researchers considering not only the sensory–motor interaction and the environment but also biological mechanisms regulating homeostasis and neural processes (Tsakiris M, Q J Exp Psychol 70(4):597–609, 2017). Historically, the body and the mind were comprehended as separate entities. The body was considered to function as a machine, responsible for providing sensory information to the mind and executing its commands. The mind, however, would process information in an isolated way, similar to a computer (Pecher D, Zwaan RA, Grounding cognition: the role of perception and action in memory, language, and thinking. Cambridge University Press, 2005). This mind and body perspective (Marmeleira J, Duarte Santos G, Percept Motor Skills 126, 2019; Marshall PJ, Child Dev Perspect 10(4):245–250, 2016), for many years, was the basis for studies in social and cognitive areas, in neuroscience, and clinical psychology