Simplified 2D CNN Architecture With Channel Selection for Emotion Recognition Using EEG Spectrogram

Farokhah, Lia; Sarno, Riyanarto; Fatichah, Chastine

doi:10.1109/access.2023.3275565

Cited by 12 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2D-CNN ( Farokhah, Sarno & Fatichah, 2023 ). 2D-CNN is the default architecture of CNN, which converts one-dimensional EEG signals into two-dimensional structures through preprocessing techniques and uses 2D-CNN for sentiment recognition to achieve classification.…”

Section: Resultsmentioning

confidence: 99%

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

Guo,

Yang,

Lin

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users’ psychological reactions and stimuli. It is important to note that the stimuli eliciting emotional responses are as critical as the responses themselves. Hence, our approach synergizes stimulus data with physical and physiological signals, pioneering a multimodal method for emotional cognition. Our proposed framework unites stimulus source data with physiological signals, aiming to enhance the accuracy and robustness of emotion recognition through data integration. We initiated an emotional cognition experiment to gather EEG and ET data alongside recording emotional responses. Building on this, we developed the Emotion-Multimodal Fusion Neural Network (E-MFNN), optimized for multimodal data fusion to process both stimulus and physiological data. We conducted extensive comparisons between our framework’s outcomes and those from existing models, also assessing various algorithmic approaches within our framework. This comparison underscores our framework’s efficacy in multimodal emotion recognition. The source code is publicly available at https://figshare.com/s/8833d837871c78542b29.

show abstract

Section: Resultsmentioning

confidence: 99%

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

Guo,

Yang,

Lin

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…For this study, the dataset is randomly split into 70 parts for training, 20 parts for validation, and 10 parts for testing. True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) are used to figure out the matrices [26]. Some of these are F1-Score, accuracy, recall, and precision.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Lia Farokhah,

Suastika Yulia Riska

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

One food item that is easy to find in nature is the mushroom. In terms of form and characteristics, mushrooms are similar. Arranging mushrooms into groups so that poisonous and non-poisonous ones can be separated is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The data set used is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16, VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. When the confusion matrix is used for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower computation architecture and cuts down on unnecessary preprocessing. This means that many people can use smartphones with more realistic data conditions to make it work.

show abstract

“…This layer contains filters and kernels to extract patterns or features from the input dataset [11]. This model presents feed-forward and backpropagation approaches similar to ANN [27]. CNN has proved to better distinguish between healthy and asthmatic subjects [10].…”

Section: Cnnmentioning

confidence: 99%

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Aulia,

Sarno,

Hidayati

et al. 2024

IJEECS

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.

show abstract

Simplified 2D CNN Architecture With Channel Selection for Emotion Recognition Using EEG Spectrogram

Abstract: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Rina Dewi Indahsari of Institut Teknologi dan Bisnis ASIA Malang under Application No. 5, and performed in line with the Emotion Analysis Data.

Cited by 12 publications

References 43 publications

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Contact Info

Product

Resources

About