EEG based Emotional State Estimation using 2-D Deep Learning Technique

Özdemir, Mehmet Akif; Degirmenci, Murside; Güren, Onan; Akan, Aydın

doi:10.1109/tiptekno.2019.8895158

Cited by 26 publications

(9 citation statements)

References 20 publications

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“…Kwon et al [35] studied the EEG signals obtained from the frontal lobe, which was then used to model an emotion recognition system using 2D CNN architectures. Ozdemir et al [36] proposed an approach for emotional state estimation using 2D CNN architectures, applied to EEG signals. The EEG signals were converted to 2D EEG images with Azimuthal Equidistant Projection (AEP) technique.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Performance Analysis of Deep Learning Models for Detection of Autism Spectrum Disorder from EEG Signals

Radhakrishnan¹,

Ramamurthy²,

Choudhury³

et al. 2021

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Autism Spectrum Disorder (ASD) starts showing symptoms in the early formative years of an individual, affecting brain development and negatively impacting social and communication skills. Subjective diagnostic methods for ASD detection require lengthy questionnaires, trained medical personnel, and occupational therapists, and are subject to observer variability. Recent years have seen a rise in the usage of machine learning techniques for detecting ASD, which stems from a requirement for objective and accurate detection methods. This research analyzes the performance of various deep convolutional architectures for the detection of ASD. The primary objective of this work is to select a method capable of performing automatic feature extraction and classification with a relatively high degree of accuracy. Several experiments were conducted with different stateof-the-art deep architectures, out of which the ResNet50 performed the best, with an average accuracy of 81%. The performances of these architectures were analyzed in terms of precision, recall, and accuracy.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Performance Analysis of Deep Learning Models for Detection of Autism Spectrum Disorder from EEG Signals

Radhakrishnan¹,

Ramamurthy²,

Choudhury³

et al. 2021

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“…The idea of the differential entropy is the compartment and all functions caused by a specific features subset [ 49 , 50 ]. By implementing this uncertainty measure, plenty of useful channels can be attained significantly.…”

Section: Methodsmentioning

confidence: 99%

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Asghar

Khan

Rizwan

et al. 2020

Sensors

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Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

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“…DEAP dataset (Koelstra et al, 2011 ) includes 32 individuals who saw 1-min long music video snippets and judged arousal/valence/like–dislike/dominance/familiarity, as well as the frontal facial recording of 22 out of 32 subjects (Chen et al, 2019b ; Ozdemir et al, 2019 ; Wilaiprasitporn et al, 2019 ; Aldayel et al, 2020 ; Gao et al, 2020a ; Liu J. et al, 2020 ).…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…Various deep learning algorithms have been employed in EEG-based BCI applications, whereas CNN is clearly the most frequent one. For example, Arnau-González et al ( 2017 ), Tang et al ( 2017 ), Vilamala et al ( 2017 ), Antoniades et al ( 2018 ), Aznan et al ( 2018 ), Behncke et al ( 2018 ), Dose et al ( 2018 ), El-Fiqi et al ( 2018 ), Nguyen and Chung ( 2018 ), Völker et al ( 2018 ), Alazrai et al ( 2019 ), Amber et al ( 2019 ), Amin et al ( 2019b ), Chen et al ( 2019a , b ), Fahimi et al ( 2019 ), Gao et al ( 2019 ), Olivas-Padilla and Chacon-Murguia ( 2019 ), Ozdemir et al ( 2019 ), Roy et al ( 2019 ), Song et al ( 2019 ), Tayeb et al ( 2019 ), Zgallai et al ( 2019 ), Zhao et al ( 2019 ), Aldayel et al ( 2020 ), Gao et al ( 2020a , b ), Hwang et al ( 2020 ), Ko et al ( 2020 ), Li Y. et al ( 2020 ), Liu J. et al ( 2020 ), Miao et al ( 2020 ), Oh et al ( 2020 ), Polat and Özerdem ( 2020 ), Atilla and Alimardani ( 2021 ), Cai et al ( 2021 ), Dang et al ( 2021 ), Deng et al ( 2021 ), Huang et al ( 2021 ), Ieracitano et al ( 2021 ), Mai et al ( 2021 ), Mammone et al ( 2021 ), Petoku and Capi ( 2021 ), Reddy et al ( 2021 ), Tiwari et al ( 2021 ), Zhang et al ( 2021 ), Ak et al ( 2022 ), and, Huang et al ( 2022 ) have explored deep learning-based algorithms. However, more recent BCI studies have implemented other deep learning modalities including,…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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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EEG based Emotional State Estimation using 2-D Deep Learning Technique

Cited by 26 publications

References 20 publications

Performance Analysis of Deep Learning Models for Detection of Autism Spectrum Disorder from EEG Signals

Performance Analysis of Deep Learning Models for Detection of Autism Spectrum Disorder from EEG Signals

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

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

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