Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Penchina, Brian; Sundaresan, Alamelu; Cheong, Sean; Grace, Victoria; Valero‐Cabré, Antoni; Martel, Adrien

doi:10.21203/rs.3.rs-112880/v1

Cited by 4 publications

(4 citation statements)

References 69 publications

(91 reference statements)

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“…We looked at numerous descriptive dimensions to investigate this question: the number of participants, the amount of EEG data collected, and the task of the datasets. There are few studies that make use of their own collected datasets (Tang et al, 2017 ; Vilamala et al, 2017 ; Antoniades et al, 2018 ; Aznan et al, 2018 ; Behncke et al, 2018 ; El-Fiqi et al, 2018 ; Nguyen and Chung, 2018 ; Alazrai et al, 2019 ; Chen et al, 2019b ; Fahimi et al, 2019 ; Hussein et al, 2019 ; Zgallai et al, 2019 ; Gao et al, 2020b ; León et al, 2020 ; Maiorana, 2020 ; Penchina et al, 2020 ; Tortora et al, 2020 ; Atilla and Alimardani, 2021 ; Cai et al, 2021 ; Cho et al, 2021 ; Mai et al, 2021 ; Mammone et al, 2021 ; Petoku and Capi, 2021 ; Reddy et al, 2021 ; Shoeibi et al, 2021 ; Sundaresan et al, 2021 ; Ak et al, 2022 ). However, most of the deep learning studies have been conducted based on publicly available EEG datasets, such as:…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…Long short-term memory (LSTM) (Zeng et al, 2018 ; Fares et al, 2019 ; Hussein et al, 2019 ; Puengdang et al, 2019 ; Saha et al, 2019 ; Chakladar et al, 2020 ; Penchina et al, 2020 ; Rammy et al, 2020 ; Tortora et al, 2020 ; Cho et al, 2021 ; Shoeibi et al, 2021 ),…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…Recurrent neural network (RNN) (Wilaiprasitporn et al, 2019 ; León et al, 2020 ; Li F. et al, 2020 ; Penchina et al, 2020 ; Mai et al, 2021 ; Sundaresan et al, 2021 ), and…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

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Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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“…EEG verileri ile epilepsi, otizm, parkinson gibi birçok nörolojik rahatsızlığın tespiti mümkündür. Nörolojik rahatsızlıkların otomatik teşhisi ile ilgili birçok çalışma mevcuttur (Brian et al, 2021;Shi, Wang, Wang, Liu, & Yan, 2019;Xu et al, 2020). Epilepsi tespitinde kullanılmak üzere birçok açık kaynak veri seti mevcuttur (Siddiqui, Morales-Menendez, Huang, & Hussain, 2020).…”

Section: Introductionunclassified

Kombine Derin Öğrenme Tabanlı Epileptik Nöbet Teşhisi

Varli¹,

Yılmaz²

2021

European Journal of Science and Technology

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Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

et al. 2021

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Neuroimaging investigations have proven that social anxiety disorder (SAD) is associated with aberrations in the connectivity of human brain functions.The assessment of the effective connectivity (EC) of the brain and its impact on the detection and medication of neurodegenerative pathophysiology is hence a crucial concern that needs to be addressed. Nevertheless, there are no clinically certain diagnostic biomarkers that can be linked to SAD. Therefore, investigating neural connectivity biomarkers of SAD based on deep learning models (DL) has a promising approach with its recent underlined potential results. In this study, an electroencephalography (EEG)-based detection model for SAD is constructed through directed causal influences combined with a deep convolutional neural network (CNN) and the long shortterm memory (LSTM). The EEG data were classified by applying three different DL models, namely, CNN, LSTM, and CNN+LSTM to discriminate the severity of SAD (severe, moderate, mild) and healthy controls (HC) at different frequency bands (delta, theta, alpha, low beta, and high beta) in the default mode network (DMN) under resting-state condition. The DL model uses the EC features as input, which are derived from the cortical correlation within different EEG rhythms for certain cortical areas that are more susceptible to SAD. Experimental results revealed that the proposed model (CNN+LSTM) outperforms the other models in SAD recognition. For our dataset, the highest recognition accuracies of 92.86%, 92.86%, 96.43%, and 89.29%, specificities of 95.24%, 95.24%, 100%, and 90.91%, and sensitivities of 85.71%, 85.71%, 87.50%, and 83.33% were achieved by using CNN+LSTM model for severe, moderate, mild, and HC, respectively. The fundamental contribution of this analysis is the characterization of neural brain features using different DL models to categorize the severity of SAD, which can represent a potential biomarker for SAD.INDEX TERMS Effective connectivity network, convolutional neural networks (CNNs), social anxiety disorder (SAD), default mode network (DMN), deep learning models, partial directed coherence (PDC), Electroencephalogram (EEG), human brain mapping.

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Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Cited by 4 publications

References 69 publications

Status of deep learning for EEG-based brain–computer interface applications

Status of deep learning for EEG-based brain–computer interface applications

Kombine Derin Öğrenme Tabanlı Epileptik Nöbet Teşhisi

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

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