Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Al-Qazzaz, Noor Kamal; Sabir, Mohannad K.; Ali, Sawal Hamid Md; Ahmad, Siti Anom; Grammer, Karl

doi:10.30880/ijie.2021.13.05.008

Cited by 2 publications

(2 citation statements)

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“…The outcome of this undertaking led to an enhancement in the training accuracy and a decrease in the validation loss. Various channel readings provide valuable insights into different types of emotions [22]. Overall, the selection of these channels is based on their proximity to brain regions consistently implicated in the processing and regulation of specific emotions.…”

Section: Datasets and Label Classificationmentioning

confidence: 99%

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

Gupta

Sharma

Bhardwaj

et al. 2023

Preprint

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In this study, a hybrid architecture combining a Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU) is proposed for multi-class emotion recognition using EEG signals.Emotion recognition using EEG signals is a challenging task due to the ever-changing nature of EEG signals and the high dimensionality of the feature space. The proposed approach aims to address these challenges by utilizing a hybrid architecture that combines the strengths of both 1D-CNN and GRU. The 1D-CNN is used to retrieve relevant spatial features from the EEG signals, while the GRU is employed to capture the temporal dependencies in the signals. The models were used to classify multi-class emotions: four and sixteen emotions based on the valence-arousal and valence-arousal-liking-dominance planes, respectively, using the benchmark DEAP dataset. The experiment results showed that the proposed models achieved high accuracy in classifying emotions for both four and sixteen emotions as compared to state of art methods. The results of this research have significant implications for the development of affective computing systems in various fields, including healthcare, human-computer interaction, and education. In conclusion, this study demonstrates the potential of deep learning models in affective computing and provides a foundation for future research in this field. The use of reliable physiological signals and the combination of different architectures have shown to be effective in accurately classifying emotions.

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Section: Datasets and Label Classificationmentioning

confidence: 99%

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

Gupta

Sharma

Bhardwaj

et al. 2023

Preprint

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“…One of the most essential indications for diagnosing ASD is the electroencephalogram (EEG). EEG signals contain a wealth of information about the brain neural activities [17]- [21].…”

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confidence: 99%

Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals

Al-Qazzaz,

Aldoori,

Buniya

et al. 2024

IEEE Access

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Autism spectrum disorder (ASD) is a developmental disease characterised by restricted and repetitive behaviours, as well as difficulty in social communication and interaction, in children. The clinical diagnosis of ASD is reached by behavioural screening, which delays early intervention. Electroencephalography (EEG) is a method for analysing the brain's electrical activity that has proven useful in the diagnosis of several neurological illnesses. Pre-trained deep Convolutional Neural Networks (CNNs) were used to extract features from the spectral profiles of the EEG dataset and classify patients into mild, moderate, and severe patients, as well as age-matched control subjects. Accordingly, the primary goal of this study is to use the pre-trained CNNs as classifiers in order to reap the benefits of transfer learning, and the secondary goal is to propose a hybrid model by employing decision tree (DT), K nearest neighbour (KNN), and a Support Vector Machine (SVM) machine learning classification techniques to categorise the features of the pre-trained CNN networks into mild, moderate, severe, and normal categories. The results show that using SqueezeNet for transfer learning improves classification accuracy to 85.5%, and that using SqueezeNet for hybrid models improves classification accuracy to 87.8% using SVM. Therefore, a hybrid model based on the combination of SqueezeNet and SVM might be utilised to automatically diagnose ASD based on the individual's EEG data. INDEX TERMS Autism, EEG, deep learning, transfer learning, convolutional neural networks, machine learningI. INTRODUCTION A UTISM spectrum disorders (ASD) are neurodevelopmental illnesses characterized by markedly aberrant social interaction, impaired communication and language skills, and narrow interests [1]. Symptoms occur throughout childhood or infancy and are usually followed by a continuous course with no recovery from an illness [2]. Symptoms appear after the age of six months, become established by the age of two or three years old, and remain into adulthood, but with less severity [3], [4]. The 'spectrum' in ASD is for indicating that autistic individuals can have a multitude of symptoms, such as difficulties in motor movement abilities, limited attention spans, and sleep and gastrointestinal disturbances [2]. These are some prevalent characteristics of children with autism in which frequently, autistic children have communication difficulties, like refusing to engage in conversation, being unable to use appropriate language, repeating what they hear, and having a low linguistic understanding [5], [6]. However, some experience sensory disturbances, as well. These disturbances might be auditory, visual, tactile, gustatory, vestibular, or proprioceptive. Additionally, they avoid social interaction, as they prefer to be alone, the majority

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Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Cited by 2 publications

References 31 publications

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

EEG Signal Based Multi Class Emotion Recognition using Hybrid 1D-CNN and GRU

Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals

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