Characterising Alzheimer’s Disease With EEG-Based Energy Landscape Analysis

Klepl, Dominik; He, Fei; Wu, Min; Marco, Matteo De; Blackburn, Daniel; Sarrigiannis, Ptolemaios G.

doi:10.1109/jbhi.2021.3105397

Cited by 25 publications

(13 citation statements)

References 27 publications

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“…AH 'yi teşhis etmek ya da karakterize etmek için erişilebilir ve invaziv olmayan yöntemler bu nedenle acilen gereklidir. EEG bu kriterleri karşılar [19,20]. 128…”

Section: Alzheimerunclassified

“…Yapılan literatür taraması sonucunda MÖ kullanılarak EEG işareti işleme konuları 18 başlık altında toplanabilir. Bunlar: epilepsi [7,8], duygu durumu [9,10], beyin-bilgisayar arayüzleri [11,12], uyku evreleri [13,14], devinimsel görselleştirme [15,16], zihinsel iş yükü [17,18], Alzheimer [19,20], Sürücü dikkati [1,21], şizofreni [3,22], robotik [23,24], dikkat [2,25], Alkol [26,27], göz kırpmak [4,28], algı [6,29], niyet [30,31], nesnelerin interneti [32,33], kişi kimliği [5,34], beyin hastalığı [35].…”

Section: Introductionunclassified

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EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

OMAR

Tepe

2022

Bayburt Üniversitesi Fen Bilimleri Dergisi

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The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning (ML). Recent advances in the field of BC have led to renewed interest in the use of electroencephalography (EEG) for different fields. EEG is used in medical and biomedical applications such as analyzing mental workload and fatigue, diagnosing brain tumors and rehabilitation of central nervous system disorders; EEG-based motion analysis and classification is widely used in many areas from clinical applications to brain-machine interface and robotic applications. This article reviews the applications of many ML algorithms used in EEG signal processing, introduces commonly used algorithms, typical application scenarios, important advances and current problems. The study explored current applications of ML in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders, and more. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural network, support vector machines, K-nearest neighbor and multidirectional convolutional neural network, are briefly explained. In addition, a general research on ML applications used in EEG analysis is presented. As a result, it was determined that the most SVM and CNN methods were used in the studies, and the study titles were mainly on epilepsy, BCI and Emotion, and the least on Alcohol, Sleeping States, Perception.

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“…AH 'yi teşhis etmek ya da karakterize etmek için erişilebilir ve invaziv olmayan yöntemler bu nedenle acilen gereklidir. EEG bu kriterleri karşılar [19,20]. 128…”

Section: Alzheimerunclassified

Section: Introductionunclassified

EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

OMAR

Tepe

2022

Bayburt Üniversitesi Fen Bilimleri Dergisi

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“…In the above research, the transformation of the EEG feature form imitated the image learning mode of deep networks, including a CNN, an RNN, and a DBN as much as possible. In addition, there are studies on EEG signal analysis by using brain function networks, such as energy landscape ( Krzemiński et al, 2020 ; Klepl et al, 2021 ), which provides a new idea for the study of EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

Zhu¹,

Wang²,

Luo³

2022

Front. Comput. Neurosci.

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Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic energy features. In this method, first, to reduce the noise superposition caused by feature analysis and extraction, the concept of an energy sequence is proposed. Second, to obtain the feature set reflecting the time persistence and multicomponent complexity of EEG signals, the construction method of the dynamic energy feature set is given. Finally, to make the network model suitable for small datasets, we used fully connected layers and bidirectional long short-term memory (Bi-LSTM) networks. To verify the effectiveness of the proposed method, we used leave one subject out (LOSO) and 10-fold cross validation (CV) strategies to carry out experiments on the SEED and DEAP datasets. The experimental results show that the accuracy of the proposed method can reach 89.42% (SEED) and 77.34% (DEAP).

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“…Emerging evidence shows large-scale alterations in functional connectivity (FC) in AD, such as an increased connectivity in the low frequency bands [8][9][10]. Graphbased studies show that AD is characterised by reduced complexity [6] and loss of small-world organisation, assessed by the clustering coefficient and characteristic path length [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on EEG. EEG has been shown to be an effective tool for studying the changes in brain activity in AD cases [5][6][7]. Compared to other modalities, EEG is economical, non-invasive, easy to administer, and has a superior temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods

Klepl

et al. 2022

Preprint

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Alzheimer's disease (AD) is the leading form of dementia in the world. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised as each aims to quantify an unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While there is a growing number of studies using GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.

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Characterising Alzheimer’s Disease With EEG-Based Energy Landscape Analysis

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 25 publications

References 27 publications

EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods

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