EEG/ERP: Within Episodic Assessment Framework for Cognition

Wallace, Bruce; Knoefel, Frank; Goubran, Rafik; Zunini, Rocío A. López; Ren, Zhaofen; Maccosham, Aaron

doi:10.1109/tim.2017.2668618

Cited by 19 publications

(7 citation statements)

References 26 publications

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“…Task performance improved for both the BrainHQ and Carleton puzzles. ( 20 , 21 ) Table 2 demonstrates that for the BrainHQ arm, the scores on the three domains utilized in this study (attention, brain speed, and memory) were significantly improved (p < .01).…”

Section: Resultsmentioning

confidence: 77%

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Implementation of a Brain Training Pilot Study For People With Mild Cognitive Impairment

et al. 2018

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ObjectiveA pilot study to determine the feasibility of recruiting patients with MCI to test for cognitive interventions.MethodThirty patients with amnestic MCI were to be divided into two intervention arms and one control group. Participants went to local sites and completed brain training for one hour three times per week for nine weeks. Outcome measures were: recruitment, computer abilities, compliance, task performance, neuropsychological tests, and electroencephalography.ResultsAfter six months, only 20 participants had been recruited. Seventeen were allocated to one of the two intervention groups. Compliance was good and computer skills were not an obstacle. Participants improved their abilities in the modules, but there were no statistically significant changes on neuropsychological tests or EEG.ConclusionsRecruitment of MCI participants for extensive cognitive intervention is challenging, but achievable. This pilot study was not powered to detect clinical changes. Future trials should consider recruitment criteria, intervention duration, scheduling, and study location.

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Section: Resultsmentioning

confidence: 77%

“…The measures for the ERP included reaction time, accuracy, and numerous ERP components. Further details are available in Lopez Zunini et al ( 18 , 19 ) and Wallace et al ( 20 )…”

Section: Methodsmentioning

confidence: 99%

Implementation of a Brain Training Pilot Study For People With Mild Cognitive Impairment

et al. 2018

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“…In this work, FBCSP was implemented using MNE-Python package, and four spatial filters were applied to decompose EEG signals into four frequency bands, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The EEG data of one subject from each class (a total of 2) were randomly selected as testing data, leaving the rest as training and validation sets with a ratio split of 13:1.…”

Section: E Frequency Domain Analysis Approachmentioning

confidence: 99%

“…The 4-task combination shows the best result only on the NC-DEM classification. As future work, we have to increase the number of the trials to reduce noise from the signals, design more complex tasks that reveal cognitive deficits better, and analyze the behavior of the patients (task performance and pressing buttons) [14], [15] along with the EEG data.…”

Section: B Time Domain Analysismentioning

confidence: 99%

“…EEG records the brain's electrical activity, which varies in frequency and amplitude, corresponding to many factors such as subjects' state (resting, thinking, memorizing), cognitive load, age, diseases, and mental tasks [10]. Several features from EEG signals, such as functional connectivity [11], [12], time-domain features [13]- [15], frequency domain features [9], [16], [17], and time-frequency features [18], are used to explore the progression of dementia and its effects on patients in several studies over the past decades. Likewise, both resting state and event-related EEG signals are exploited to investigate neurological disorders.…”

Section: Introductionmentioning

confidence: 99%

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A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

Kongwudhikunakorn,

Kiatthaveephong,

Thanontip

et al. 2021

Preprint

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Dementia is one of the main causes of cognitive decline. Since the majority of dementia patients cannot be cured, being able to diagnose them before the onset of the symptoms can prevent the rapid progression of the cognitive impairment. This study aims to investigate the difference in the Electroencephalograph (EEG) signals of three groups of subjects: Normal Control (NC), Mild Cognitive Impairment (MCI), and Dementia (DEM). Unlike previous works that focus on the diagnosis of Alzheimer's disease (AD) from EEG signals, we study the detection of dementia to generalize the classification models to other types of dementia. We have developed a pilot study on machine learning-based dementia diagnosis using EEG signals from four visual stimulation tasks (Fixation, Mental Imagery, Symbol Recognition, and Visually Evoked Related Potential) to identify the most suitable task and method to detect dementia using EEG signals. We extracted both frequency and time domain features from the EEG signals and applied a Support Vector Machine (SVM) for each domain to classify the patients using those extracted features. Additionally, we study the feasibility of the Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia. The evaluation of the model shows that the tasks that test the working memory are the most appropriate to detect dementia using EEG signals in both time and frequency domain analysis. However, the best results in both domains are obtained by combining features of all four cognitive tasks.

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One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram

Saini

Satija²,

Upadhayay³

2022

Biomedical Signal Processing and Control

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EEG/ERP: Within Episodic Assessment Framework for Cognition

Cited by 19 publications

References 26 publications

Implementation of a Brain Training Pilot Study For People With Mild Cognitive Impairment

Implementation of a Brain Training Pilot Study For People With Mild Cognitive Impairment

A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition

One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram

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