Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition

Cheah, Kit Hwa; Nisar, Humaira; Yap, Vooi Voon; Lee, Chen-Yi; Sinha, G. R.

doi:10.1155/2021/5599615

Cited by 33 publications

(18 citation statements)

References 43 publications

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“…It utilized a sparse matrix as input to reflect the relative position of the electrodes. Compared with complex input of RNNs and 2D/3D CNNs, Cheah et al [29] proposed a 1D-CNN based ResNet18, which adopted simple input(channel × time) to train the deeper neural network. It is more suitable to perform pre-training with simple data processing and a faster training process.…”

Section: Related Work a Eeg-based Emotion Recognitionmentioning

confidence: 99%

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Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition

Kan¹,

Yu²,

Huang³

et al. 2022

Preprint

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The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel geneticsinspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG samples in a group for generating augmented groups by pairing, cross exchanging, and separating. And the model adopts a group projector to extract group-level feature representations from group EEG samples triggered by the same emotion video stimuli. Then contrastive learning is employed to maximize the similarity of group-level representations of augmented groups with the same stimuli. The SGMC achieves the state-of-the-art emotion recognition results on the publicly available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and arousal dimensions, and also reaches competitive performance on the public SEED dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows significant performance even when using limited labels. Moreover, the results of feature visualization suggest that the model might have learned video-level emotionrelated feature representations to improve emotion recognition. And the effects of group size are further evaluated in the hyper parametric analysis. Finally, a control experiment and ablation study are carried out to examine the rationality of architecture. The code is provided publicly online 1 .

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Section: Related Work a Eeg-based Emotion Recognitionmentioning

confidence: 99%

“…We introduce the base encoder f : R M×C → R D which map individual EEG sample X to its representation h on a 512-dimensional feature space. Based on the existing model ResNet18-1D [29], the base encoder is designed as follows:…”

Section: Base Encodermentioning

confidence: 99%

Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition

Kan¹,

Yu²,

Huang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Two experiments were carried out on the SEED dataset and the average classification accuracy was 92.02 and 82.14%. Cheah et al (2021) proposed an EEG emotion recognition algorithm based on residual networks (ResNet), which achieved 93.42% accuracy on the SEED dataset.…”

Section: Emotion Recognition Based On Database For Emotion Analysis Using Physiological Signalsmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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“…Sample images after applying CLAHE are shown in Figure 3. the information acquired from a pre-trained network, which can then be transferred to a new model for validation (Cheah et al, 2021;Le et al, 2021;Saba et al, 2020).…”

Section: Contrast Limited Adaptive Equalization (Clahe)mentioning

confidence: 99%

Automated COVID‐19 detection in chest X‐ray images using fine‐tuned deep learning architectures

et al. 2021

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The COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerasechain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used.Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

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Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition

Cited by 33 publications

References 43 publications

Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition

Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition

Review on Emotion Recognition Based on Electroencephalography

Automated COVID‐19 detection in chest X‐ray images using fine‐tuned deep learning architectures

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