Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Bhat, Shreya; Acharya, U. Rajendra; Dadmehr, Nahid; Adeli, Hojjat

doi:10.1159/000441447

Cited by 52 publications

(23 citation statements)

References 93 publications

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“…Cognitive deficits progress to dementia in up to 60% of PD patients [2][3][4][5]. AD and PD are due to progressive neurodegenerative pathologies associated with an abnormal accumulation of proteins in the brain (i.e., A␤ extracellularly and phosphorylated tau protein and ␣-synuclein intracellularly), causing axonal dysfunction, neuronal loss, and brain atrophy [6]. AD can be detected even in the prodromal stage of mild cognitive impairment (MCI) using cerebrospinal fluid (CSF) and positron emission tomography (PET) diagnostic biomarkers of A␤ and phospho-tau [7].…”

Section: Introductionmentioning

confidence: 99%

Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer’s and Parkinson’s Diseases: An EEG Study

Babiloni

Percio

Lizio

et al. 2017

JAD

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Abstract. The aim of this retrospective and exploratory study was that the cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms might reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and Parkinson's disease (PDMCI) as compared to healthy subjects. Clinical and rsEEG data of 75 ADMCI, 75 PDMCI, and 75 cognitively normal elderly (Nold) subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini-Mental State Evaluation (MMSE) was matched between the ADMCI and PDMCI groups. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources. Receiver operating characteristic curve (ROC) classified these sources across individuals. Results showed that compared to the Nold group, the posterior alpha2 and alpha3 source activities were more abnormal in the ADMCI than the PDMCI group, while the parietal delta source activities were more abnormal in the PDMCI than the ADMCI group. The parietal delta and alpha sources correlated with MMSE score and correctly classified the Nold and diseased individuals (area under the ROC = 0.77-0.79). In conclusion, the PDMCI and ADMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness. Future prospective cross-validation studies will have to test these rsEEG markers for clinical applications and drug discovery.

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Section: Introductionmentioning

confidence: 99%

Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer’s and Parkinson’s Diseases: An EEG Study

Babiloni

Percio

Lizio

et al. 2017

JAD

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show abstract

Section: Introductionmentioning

confidence: 99%

“…Typically, these methods involve measuring some 4 aspect of neural activity and inferring or decoding an intended action or particular 5 characteristic of the user's cognitive state. Although BCI is still in its infancy, there are 6 already practical applications in assistive technology as well as disease diagnosis [2,3]. 7Brain-controlled prosthetics [4] and spellers [5] have shown their potential to enable 8 natural interaction in comparison with more traditional methods, such as mechanical 9 prosthetics or eye-movement-based spellers.…”

mentioning

confidence: 99%

Towards a real-world brain-computer interface for image retrieval

McCartney

Rincón

Devereux

et al. 2019

Preprint

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Brain decoding -the process of inferring a person's momentary cognitive state from their brain activity -has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically inspired computer vision and linguistic models. We apply this approach to solve the problem of identifying viewed images from recorded brain activity in a reliable and scalable way. We demonstrate competitive decoding accuracies across two EEG datasets, using a zero-shot learning framework more applicable to real-world image retrieval than traditional classification techniques. Introduction 1Research in the field of Brain-Computer Interfaces (BCI) began in the 1970s [1] with 2 the aim of providing a new, intuitive, and rich method of communication between 3 computer systems and their users. Typically, these methods involve measuring some 4 aspect of neural activity and inferring or decoding an intended action or particular 5 characteristic of the user's cognitive state. Although BCI is still in its infancy, there are 6 already practical applications in assistive technology as well as disease diagnosis [2,3]. 7Brain-controlled prosthetics [4] and spellers [5] have shown their potential to enable 8 natural interaction in comparison with more traditional methods, such as mechanical 9 prosthetics or eye-movement-based spellers. Other relevant applications include 10 identifying the image that a user is viewing, usually referred as image retrieval, of 11 particular interest in the field of visual attention applied to advertising and marketing, 12 searching and organising large collections of images, or reducing distractions during 13 driving, to name a few.14 Although brain decoding technology has immense potential in diverse applications, it 15 faces multiple challenges related to speed and accuracy that must be overcome before it 16 emerges as a disruptive technology. The complexity of BCI stems from the naturally low 17 signal-to-noise ratio (SNR) and high dimensionality of raw brain data, which often 18 complicates automated analysis and can force researchers to manually analyse 19 previously recorded neural activation data. This is typically done either by examining 20 the frequency domain or by plotting Event-Related Potentials (ERPs). In an ERP 21 experiment the participant will be presented several times with a similar stimulus and 22 their neural response each time can be recorded and averaged. These ERPs can be 23 analysed against well-known response patterns or, alternatively, characteristics such as 24 February 27, 2019 1/18 the strength and timing of signal peaks can be quantified and analysed automatically. 25ERP analysis is well established and has strong applications in medical diagnosis [6] and 26 in cognitive neuroscience research [7,8]; however, the broad characterisation of brain 27 response used in traditional ERP methods is not ...

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“…Additionally, in many neurological studies, synchronization refers to a greatly important mechanism which can help to describe information processing for a typical or atypical brain [13][14][15][16]. Usually, the neural signals in different brain areas were recorded by employing multiple electrodes simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, an increasing amount of evidence, from physiological and electrophysiological studies, has proven that abnormalities in the synchronous oscillatory activity of neurons may dominate in the pathophysiology of brain disorder [15,16,20,21], and these will be reflected in EEG signals. Therefore, to evaluate the cooperation strength between neurons or cortical networks, a common approach is to measure them from EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Global Synchronization of Multichannel EEG Based on Rényi Entropy in Children with Autism Spectrum Disorder

Han

Kang

et al. 2017

Applied Sciences

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Autism spectrum disorder (ASD) has been defined as a pervasive neurodevelopmental disorder, involving communication, social interaction and repetitive behaviors. Currently, it is still challenging to understand the differences of brain activity between ASD and healthy children. In this study, we propose calculating the Rényi entropy of the eigenvalues derived from the signal correlation matrix to measure the global synchronization in multichannel electroencephalograph (EEG) from 16 children with ASD (aged 8-12 years) and 16 age-and sex-matched healthy controls at the resting state. The results indicate that there is a significantly diminished global synchronization from ASD to healthy control. The proposed method can help to reveal the intrinsic characteristics of multichannel EEG signals in children with ASD and aspects that distinguish them from healthy children.

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Clinical Neurophysiological and Automated EEG-Based Diagnosis of the Alzheimer's Disease

Cited by 52 publications

References 93 publications

Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer’s and Parkinson’s Diseases: An EEG Study

Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer’s and Parkinson’s Diseases: An EEG Study

Towards a real-world brain-computer interface for image retrieval

Global Synchronization of Multichannel EEG Based on Rényi Entropy in Children with Autism Spectrum Disorder

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