ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude (�100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure

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%

Understanding the temporal dynamics of visual hallucinations in Parkinson's Disease with dementia

PhD ThesisBackground Integrative models of visual hallucinations (VH) posit that the symptom requires disruptions to both bottom-up and top-down visual processing. Although many lines of evidence point to a mixture of aberrant processing and disconnection between key nodes in the visual system, in particular the dorsal and ventral attention networks, there have been no attempts to understand the dynamic behaviour of these systems in Parkinson’s disease with dementia (PDD) with VH. Aims The primary aim of this thesis was to explore the correlates of synaptic communication in the visual system and how spatio-temporal dynamics of the early visual system are altered in relation to the severity of VH. The secondary aim was to help understand the balance between the contributions of bottom-up and top-down processing for the experience of VH in PDD. Methods An assortment of investigative approaches, including resting state electroencephalography (EEG), visual evoked potentials (VEPs), and concurrent EEG and transcranial magnetic stimulation (TMS) were applied in a group of PDD patients with a range of VH severities (n = 26) and contrasted with a group of age matched healthy controls (n = 17). Results Latency of the N1 component was similar between groups, suggesting intact transfer between the retina and the cortex. However, PDD patients had an inherent reduction in the amplitude of the VEP components and displayed a pattern of declining P1 latencies in association with more frequent and severe VH. Evoked potentials arising from TMS of the striate cortex were similar in amplitude and latency for each of the components between PDD and controls. However, inter-component activity at several stages was altered in the PDD group, whilst the frequency and severity of VH was positively associated with the amplitudes of several components in the occipital and parietal regions. Finally, attentional modulation as measured by the alpha-band reactivity was also compromised in PDD patients. iv Conclusions These data provide neurophysiological evidence that both early bottom-up and top-down dysfunctions of the visual system occur in PDD patients who hallucinate, thus supporting integrative models of VH.National Institute for Health Research (NIHR) Biomedical Research Unit (BRU)

Electroencephalogram classification of brain states using deep learning approach

The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing techniques, input type, and the depth of learning. This study summarises the current methodologies and performance results in EEG categorization using deep learning. A series of practical recommendations is provided in the hopes of encouraging or directing future research using EEG datasets to use deep learning

Large‐scale collaboration in ENIGMA‐EEG: A perspective on the meta‐analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity.

Background and purpose The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability

Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity

BACKGROUND AND PURPOSE: The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. METHODS: We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. RESULTS: We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. CONCLUSION: The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability

Hand classification of fMRI ICA noise components

We present a practical "how-to" guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets

Investigating attentional function and cognitive fluctuations in Lewy body dementia

PhD ThesisLewy body dementias (LBD), which include dementia with Lewy bodies (DLB) and Parkinson’s disease with dementia (PDD), are characterised by attentional dysfunction and fluctuating cognition. The underlying aetiology of these clinical features is poorly understood, yet such knowledge is essential for developing effective management strategies. The aim of this project was to determine the specific facets of attention affected in LBD patients, and to use high-density electroencephalography (EEG) to delineate the underlying pathophysiology and how this relates to cognitive fluctuations. Methods: Attentional network efficiency was investigated in LBD patients (n = 32), Alzheimer’s disease (AD) patients (n = 27), and age-matched healthy controls (n = 21) by using a modified version of the Attention Network Test (ANT). The ANT, a visual attention task, probes the efficiency of three anatomically defined attentional networks: alerting, orienting and executive conflict. Participants completed the ANT whilst undergoing EEG recordings (128 channels). In a subsample of the participants (22 DLB, 24 AD, 19 controls), time-frequency wavelet analyses were conducted to investigate event-related spectral perturbations (ERSP), between 4-90 Hz, in the 500 ms post-stimuli presentation. Attentional network ERSP was calculated by contrasting the oscillatory reactivity following relevant stimuli. Results: Overall mean reaction time was slower in the dementia groups (AD and LBD) relative to the controls, and the LBD group were slower than the AD group. Behaviourally, there were no group differences regarding the orienting effect. However, both dementia groups exhibited reduced executive conflict processing efficiency, and a lack of an alerting effect. Electrophysiologically, the DLB group exhibited a profound lack of post-stimulus oscillatory reactivity below 30 Hz, irrespective of stimulus condition. For the alerting network, the DLB group exhibited attenuated reactivity in the lower frequencies (< 30 Hz); in the theta range (4-7 Hz) the controls and AD group showed global synchronisation (across all regions), peaking at approximately 300 ms, which was absent in the DLB group. Lack of DLB theta synchronisation between 200-450 ms over the right parietal cortex was associated with a ii higher total score on the Clinical Assessment of Fluctuation scale. Orienting and executive conflict network reactivity was comparable across all groups; primarily intermittent synchronisation, of reduced power relative to the alerting network, diffuse across the time and frequency domains in all regions. Conclusions: Attenuated global oscillatory reactivity in the DLB group specific to the alerting network (the network associated with the ability to maintain an alert state) is indicative of this fractionated aspect of attention being differentially affected in the DLB patients relative to the AD and control groups. Lack of theta reactivity in the parietal regions may contribute to the underlying pathophysiology of cognitive fluctuations in DLB.Alzheimer’s Research U

Comparative analysis of TMS-EEG signal using different approaches in healthy subjects

openThe integration of transcranial magnetic stimulation with electroencephalography (TMS-EEG) represents a useful non-invasive approach to assess cortical excitability, plasticity and intra-cortical connectivity in humans in physiological and pathological conditions. However, biological and environmental noise sources can contaminate the TMS-evoked potentials (TEPs). Therefore, signal preprocessing represents a fundamental step in the analysis of these potentials and is critical to remove artefactual components while preserving the physiological brain activity. The objective of the present study is to evaluate the effects of different signal processing pipelines, (namely Leodori et al., Rogasch et al., Mutanen et al.) applied on TEPs recorded in five healthy volunteers after TMS stimulation of the primary motor cortex (M1) of the dominant hemisphere. These pipelines were used and compared to remove artifacts and improve the quality of the recorded signals, laying the foundation for subsequent analyses. Various algorithms, such as Independent Component Analysis (ICA), SOUND, and SSP-SIR, were used in each pipeline. Furthermore, after signal preprocessing, current localization was performed to map the TMS-induced neural activation in the cortex. This methodology provided valuable information on the spatial distribution of activity and further validated the effectiveness of the signal cleaning pipelines. Comparing the effects of the different pipelines on the same dataset, we observed considerable variability in how the pipelines affect various signal characteristics. We observed significant differences in the effects on signal amplitude and in the identification and characterisation of peaks of interest, i.e., P30, N45, P60, N100, P180. The identification and characteristics of these peaks showed variability, especially with regard to the early peaks, which reflect the cortical excitability of the stimulated area and are the more affected by biological and stimulation-related artifacts. Despite these differences, the topographies and source localisation, which are the most informative and useful in reconstructing signal dynamics, were consistent and reliable between the different pipelines considered. The results suggest that the existing methodologies for analysing TEPs produce different effects on the data, but are all capable of reproducing the dynamics of the signal and its components. Future studies evaluating different signal preprocessing methods in larger populations are needed to determine an appropriate workflow that can be shared through the scientific community, in order to make the results obtained in different centres comparable

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