Exploring the role of dry-EEG and machine learning methods to profile mild cognitive impairment
type: CreativeWork
description: Background: Alzheimer’s Research UK reports 850,000 individuals are living with dementia in the UK today and this is estimated to increase to one million by 2025. Alzheimer’s Disease (AD), a type of dementia, accounts for approximately 60% of all diagnoses and as yet, there is no cure. Diagnosis rates in high income countries are below half and achieving acceptable levels of coverage and access to care is the challenge currently being faced (World Health Organization, 2015). Mild cognitive impairment (MCI) is a pre-clinical form of AD (Petersen, 2011) and is manifested by mild symptoms affecting cognitive function, without interference in daily activities. Neurodegeneration, defined by synaptic dysfunction, occurs decades prior to any symptoms emerging (Albert et al., 2011) interrupting electrical communication between neurones. Methods: A multi assessment approach using a wireless, dry-EEG system was used to explore the potential of recording brain activity outside the laboratory and in a more informal setting. Participants with MCI and neurotypical controls completed a neuropsychological assessment and computerized tasks designed to probe cognitive function where deficits are expected in MCI. Event related potential technique was applied to the EEG and features of interest were extracted for further analysis using supervised machine learning methods. Analysis and Results: Analysis was carried out on a group level and individual level. The latter involved training algorithms on a range of data models to identify patterns in the data with feature combinations predicting group membership of individuals with accuracy. Randomisation tests confirmed that there no differences found at a group level when considering the groups’ performance during episodic memory, language, executive function (working memory) or attention tasks. For analysis at an individual level, improved model accuracies with the addition of ERP features to neuropsychological features were reported for …
abstract: Background: Alzheimer’s Research UK reports 850,000 individuals are living with dementia in the UK today and this is estimated to increase to one million by 2025. Alzheimer’s Disease (AD), a type of dementia, accounts for approximately 60% of all diagnoses and as yet, there is no cure. Diagnosis rates in high income countries are below half and achieving acceptable levels of coverage and access to care is the challenge currently being faced (World Health Organization, 2015). Mild cognitive impairment (MCI) is a pre-clinical form of AD (Petersen, 2011) and is manifested by mild symptoms affecting cognitive function, without interference in daily activities. Neurodegeneration, defined by synaptic dysfunction, occurs decades prior to any symptoms emerging (Albert et al., 2011) interrupting electrical communication between neurones. Methods: A multi assessment approach using a wireless, dry-EEG system was used to explore the potential of recording brain activity outside the laboratory and in a more informal setting. Participants with MCI and neurotypical controls completed a neuropsychological assessment and computerized tasks designed to probe cognitive function where deficits are expected in MCI. Event related potential technique was applied to the EEG and features of interest were extracted for further analysis using supervised machine learning methods. Analysis and Results: Analysis was carried out on a group level and individual level. The latter involved training algorithms on a range of data models to identify patterns in the data with feature combinations predicting group membership of individuals with accuracy. Randomisation tests confirmed that there no differences found at a group level when considering the groups’ performance during episodic memory, language, executive function (working memory) or attention tasks. For analysis at an individual level, improved model accuracies with the addition of ERP features to neuropsychological features were reported for …
author: Toogood, Alison
language: eng
keywords: Electroencephalography, event related potential, mild cognitive impairment, N400, P300, subsequnet memory effect, ERP, MCI, EEG, episodic memory, language, working memory
date published: 2021-7
date modified: 2026-07-24T03:55:51Z
provider: Queen's University Belfast
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queen's University Belfast