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 classificationmodels to other types of dementia. We have developed a pilot studyon 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 .
In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP) . We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results give essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients .
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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
In the status quo, dementia is yet to be cured. Precise diagnosis prior to
A
emerging
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This study aims to investigate the difference in the Electroencephalograph
Recent progress has shown that Electroencephalography
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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 classificationmodels to other types of dementia. We have developed a pilot studyon 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
is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory,
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applied a Support Vector Machine (SVM)for each domain to classify the patients using those extracted features. Additionally, we study the feasibility of the
executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and
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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
. We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results give essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients