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

Kongwudhikunakorn, Supavit; Kiatthaveephong, Suktipol; Thanontip, Kamonwan; Leelaarporn, Pitshaporn; Piriyajitakonkij, Maytus; Charoenpattarawut, Thananya; Autthasan, Phairot; Chaisaen, Rattanaphon; Dujada, Pathitta; Sudhawiyangkul, Thapanun; Senanarong, Vorapun; Wilaiprasitporn, Theerawit

doi:10.1109/tim.2021.3120131

Cited by 16 publications

(6 citation statements)

References 42 publications

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“…Various previous researches have demonstrated the validity of SVM application to EEG [ 15 , 25 , 26 ]. Therefore, we adopted SVM to realize classifications in this manuscript.…”

Section: Methodsmentioning

confidence: 99%

“…Support vector machine (SVM) [ 13 ] is a machine learning model with nonlinear classification ability and has been successfully applied to EEG classification in various scenes. For example, Turnip et al used SVM separate lying subjects from the innocent one based on signal P300 [ 14 , 15 ]. In this work, we tried SVM for classification.…”

Section: Introductionmentioning

confidence: 99%

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Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Huang

Dang

et al. 2022

Computational and Mathematical Methods in Medicine

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Electroencephalogram (EEG) plays a crucial role in the study of working memory, which involves the complex coordination of brain regions. In this research, we designed and conducted series of experiments of memory with various memory loads or target forms and collected behavioral data as well as 32-lead EEG simultaneously. Combined with behavioral data analysis, we segmented EEG into slices; then, we calculated phase-locking value (PLV) of Gamma rhythms between every two leads, conducted binarization, constructed brain function network, and extracted three network characteristics of node degree, local clustering coefficient, and betweenness centrality. Finally, we inputted these network characteristics of all leads into support vector machines (SVM) for classification and obtained decent performances; i.e., all classification accuracies are greater than 0.78 on an independent test set. Particularly, PLV application was restricted to the narrow-band signals, and rare successful application to EEG Gamma rhythm, defined as wide as 30-100 Hz, had been reported. In order to address this limitation, we adopted simulation on band-pass filtered noise with the same frequency band as Gamma to help determine the PLV binarizing threshold. It turns out that network characteristics based on binarized PLV have the ability to distinguish the presence or absence of memory, as well as the intensity of the mental workload at the moment of memory. This work sheds a light upon phase-locking investigation between relatively wide-band signals, as well as memory research via EEG.

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“…Various previous researches have demonstrated the validity of SVM application to EEG [ 15 , 25 , 26 ]. Therefore, we adopted SVM to realize classifications in this manuscript.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Huang

Dang

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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“…Consequently, classifcation of sleep stages, investigation of heart rate variability, REM sleep without atonia (RSWA), polysomnography (PSG), electrooculography (EOG), electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) have received less attention in detecting arousal (EMG). To make easier the recognition of neurocognitive disorders, EEG is a cost-efective and promising test [6]. Trough activation of the autonomic nervous system (ANS), cortical arousals are linked to fuctuations in heart rate and blood pressure [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

Badiei

Meshgini

Rezaee

2023

Computational Intelligence and Neuroscience

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The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%–7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.

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“…Different EEG frequencies are discussed in existing studies for cognitive tasks [4], [5] as the researchers want to find the brain condition in different tasks. Few studies have discussed the delta band for cognition as various frequencies are involved in cognitive tasks [6], [7]. Moreover, their main focus was only on the frontal region, but in this study, we focused on both the frontal and occipital regions and mainly targeted the fast-wave activity [8].…”

Section: Introductionmentioning

confidence: 99%

Power Spectrum of Brain for Complex Task using Electroencephalogram

Zafar

Rehman

Dilpazir

2022

IJIST

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Cognitive processes are constantly under critical relevance in the field of neuroscience. In the examination of Electroencephalogram (EEG) data, frequency bands are always significant because they determine how the brain responds to various activities and circumstances in different situations. However, it is usually accepted that each brain region is associated with a certain activity, such as auditory, visual, or cognitive tasks. Moreover, to retrieve additional information from the brain, it is often required to establish new neural connections. In this study, the researchers established a functional relationship between different EEG frequencies and the cognitive task under investigation. The frequencies of alpha, beta, and theta waves are often discussed in connection to cognitive tasks, but the frequency of the delta wave is seldom referenced throughout the cognitive process. We then focused on the frequency of delta waves in different parts of the brain, such as the occipital and front all regions

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

Cited by 16 publications

References 42 publications

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

Power Spectrum of Brain for Complex Task using Electroencephalogram

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