EEG signal analysis via a cleaning procedure based on multivariate empirical mode decomposition

IJCCI 2012Artifacts are present in most of the electroencephalography (EEG) recordings, making it difficult to interpret or analyze the data. In this paper a cleaning procedure based on a multivariate extension of empirical mode decomposition is used to improve the quality of the data. This is achieved by applying the cleaning method to raw EEG data. Then, a synchrony measure is applied on the raw and the clean data in order to compare the improvement of the classification rate. Two classifiers are used, linear discriminant analysis and neural networks. For both cases, the classification rate is improved about 20%

New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer's disease

Les malalties neurodegeneratives són un conjunt de malalties que afecten al cervell. Aquestes malalties estan relacionades amb la pèrdua progressiva de l'estructura o la funció de les neurones, incloent-hi la mort d'aquestes. La malaltia de l'Alzheimer és una de les malalties neurodegeneratives més comunes. Actualment, no es coneix cap cura per a l'Alzheimer, però es creu que hi ha un grup de medicaments que el que fan és retardar-ne els principals símptomes. Aquests s'han de prendre en les primeres fases de la malaltia ja que sinó no tenen efecte. Per tant, el diagnòstic precoç de la malaltia de l'Alzheimer és un factor clau. En aquesta tesis doctoral s'han estudiat diferents aspectes relacionats amb la neurociència per investigar diferents eines que permetin realitzar un diagnòstic precoç de la malaltia en qüestió. Per fer-ho, s'han treballat diferents aspectes com el preprocessament de dades, l'extracció de característiques, la selecció de característiques i la seva posterior classificació.Neurodegenerative diseases are a group of disorders that affect the brain. These diseases are related with changes in the brain that lead to loss of brain structure or loss of neurons, including the dead of some neurons. Alzheimer's disease (AD) is one of the most well-known neurodegenerative diseases. Nowadays there is no cure for this disease. However, there are some medicaments that may delay the symptoms if they are used during the first stages of the disease, otherwise they have no effect. Therefore early diagnose is presented as a key factor. This PhD thesis works different aspects related with neuroscience, in order to develop new methods for the early diagnose of AD. Different aspects have been investigated, such as signal preprocessing, feature extraction, feature selection and its classification

Low-Density EEG Correction With Multivariate Decomposition and Subspace Reconstruction

A hybrid method is proposed for removing artifacts from electroencephalographic (EEG) signals. This relies on the integration of artifact subspace reconstruction (ASR) with multivariate empirical mode decomposition (EMD). The method can be applied when few EEG sensors are available, a condition in which existing techniques are not effective, and it was tested with two public datasets: 1) semisynthetic data and 2) experimental data with artifacts. One to four EEG sensors were taken into account, and the proposal was compared to both ASR and multivariate EMD (MEMD) alone. The proposed method efficiently removed muscular, ocular, or eye-blink artifacts on both semisynthetic and experimental data. Unexpectedly, the ASR alone also showed compatible performance on semisynthetic data. However, ASR did not work properly when experimental data were considered. Finally, MEMD was found less effective than both ASR and MEMD-ASR

Eliminació d'artefactes en EGG mitjançant l'ús de la Multivariate Empirical Mode Decomposition

Curs 2010-2011La tècnica de l’electroencefalograma (EEG) és una de les tècniques més utilitzades per estudiar el cervell. En aquesta tècnica s’enregistren els senyals elèctrics que es produeixen en el còrtex humà a través d’elèctrodes col•locats al cap. Aquesta tècnica, però, presenta algunes limitacions a l’hora de realitzar els enregistraments, la principal limitació es coneix com a artefactes, que són senyals indesitjats que es mesclen amb els senyals EEG. L’objectiu d’aquest treball de final de màster és presentar tres nous mètodes de neteja d’artefactes que poden ser aplicats en EEG. Aquests estan basats en l’aplicació de la Multivariate Empirical Mode Decomposition, que és una nova tècnica utilitzada per al processament de senyal. Els mètodes de neteja proposats s’apliquen a dades EEG simulades que contenen artefactes (pestanyeigs), i un cop s’han aplicat els procediments de neteja es comparen amb dades EEG que no tenen pestanyeigs, per comprovar quina millora presenten. Posteriorment, dos dels tres mètodes de neteja proposats s’apliquen sobre dades EEG reals. Les conclusions que s’han extret del treball són que dos dels nous procediments de neteja proposats es poden utilitzar per realitzar el preprocessament de dades reals per eliminar pestanyeigs.Abstract The electroencephalogram (EEG) is one of the most used techniques to study the brain. This technique records the electric potentials generated in the human cortex with electrodes attached to the scalp. However, this technique presents several shortcomings. The more important shortcoming is the presence of artifacts, which are undesired signals that disturb the EEG time series. These artifacts are due to muscle action. The aim of this Master Final Project is to present three new procedures to clean artifacts of EEG data. The new procedures are based on the application of the Multivariate Empirical Mode Decomposition, which is a new technique used in data processing. The proposed methods are applied to simulated EEG data with artifacts (eye blinks). Once the cleaning methods are applied, clean data is compared with EEG data without eye blinks to quantify the improvement of the data. Subsequently, two of the presented methods are applied to real data to show that the procedures can be applied to actual recordings. The results point out that the use of two of the cleaning procedures proposed to correct eye blinks may be a good procedure for EEG signal preprocessing.Director/a: Jordi Solé Casal

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Analysis of Small Muscle Movement Effects on EEG Signals

In this thesis, the artefactual effects of the small muscle movements were investigated. Upper frequency bands (30 Hz) of the EEG signal were extracted in order to investigate the artefactual effects of the small muscle movements. When the contamination level is high, the detection of the small muscle artifact can be made with the 92.2% accuracy. If these artifacts are really small such as a single finger movement, the detection accuracy decreases to 64%. But, the detection accuracy increases to 72% after removing the eye blink artifacts. The results of the classification support our hypothesis about the artefactual effects of the small muscle movements

An investigation into the mechanisms of inter-brain synchrony during early social interactions

Over the last 20 years there has been a growing increase in the amount of research investigating how and why two or more individual’s brain activity can synchronise during social interaction. What we know so far from this research is that inter-brain synchrony (defined through temporally coordinated patterns of brain activity between two interacting individuals, Holroyd 2022) tends to associate with moments of behavioural coordination (i.e., when two individuals are doing or attending to the same thing at the same time) and task cooperation (i.e., the action or process of two individuals working together to the same end). These observations have led many researchers to theorise over whether and how behavioural coordination mechanistically drives inter-brain synchrony (Wass et al., 2020; Hamilton, 2021). There is also some very recent evidence to suggest that increased inter-brain synchrony actually facilitates/ supports aspects of social interaction. For example, inter-brain synchrony has been shown to predict team performance (Reinero et al., 2021), although this research is primarily based on correlational study designs. Taken together however the field of inter-brain synchrony shares one fundamental limitation; that is that it does not account (although see recent animal research e.g., Kingsbury et al., 2019; Zhang et al., 2019), empirically for the mechanisms that give rise to inter-brain synchrony, which would help to falsify claims that inter-brain synchrony is a core mechanism facilitating social interaction. This is because of two main reasons; Firstly, the study of inter-brain synchrony has primarily been investigated as a time-invariant property, almost no studies have explored how inter-brain synchrony varies over time relative to individual moments of behavioural coordination. Secondly, little attention has been paid to the changes in the underlying signal properties (i.e., increases in power, changes in frequency) that must take place for two unsynchronised signals to become synchronised (e.g., Haresign et al., 2022). Using two-person naturalistic biobehavioural recording techniques, coupled with state of the art, EEG pre-processing and analyses procedures (see chapters 5 and 6), the present thesis examines the mechanisms that give rise to inter-brain synchrony during parent-infant social interactions. Evidence is presented showing how inter-brain synchrony does not arise around individual moments of gaze coordination. This is despite previous investigations suggesting that increased inter-brain synchrony (averaged over all moments of eye contact) associates with gaze synchrony. Evidence also shows the contribution of behavioural coordination across multiple modalities to inter-brain synchrony during parent-infant social interaction. Discussion is focused on the contribution of these findings to our understanding of the mechanisms that give rise to inter-brain synchrony