A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Abdulrahman, Awf; Baykara, Muhammet

doi:10.18280/ts.380430

Cited by 16 publications

(7 citation statements)

References 53 publications

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“…[6]. The studies utilized machine learning algorithms such as Gaussian Mixture Model (GMM), clustering algorithms like K means, Machine learning classifiers like the support vector machine, and artificial neural networks like ELM's (Extreme learning machine) [7] to classify emotional EEG data into different categories. The research also involved pre-processing techniques such as bandpass filtering, Welch period technique, Digital Signal Processing (DSP), Power Spectral Density, Wavelet Decomposition and transforms, and Hjorth Parameter for extracting feature vectors from filtered EEG data [8].…”

Section: Related Workmentioning

confidence: 99%

Emotion Detection using Stack Auto Encoder, Deep Learning and LSTM Model

Shah,

Rane,

Kinger

2024

IJCDS

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There is an increasing need for distress emotion acknowledgement and channeling the same is very important. There is an increasing need for machines to understand human and their complex emotions deeply. This research describes a unique framework for emotion detection that helps brain-computer interface/machine (BCI) to understand human emotions and brain complexity and working using multi-channel electroencephalograms (EEG). We have employed two datasets in our work, Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-Cost Off-the-Shelf Devices (DREAMER) and dataset for emotion analysis using EEG, physiological and video signals (DEAP) for LSTM model implementation and validation respectively. A linear model is considered for EEG signal mixing and an emotion timing model comprise the framework. Leveraging the contextual correlations within EEG feature sequences, our proposed methodology effectively recovers EEG source signals from the obtained EEG signals, thereby improving the accuracy rate of classification. Also, stress bins were set up for individual users to assess their degree of stress and calmness following exposure to external stimuli. DEAP dataset using LSTM framework was enforced for emotion recognition, and mean recognition accuracy using area under curve as evaluation matrix for valance and arousal was 82.02% and 76.52% respectively, validating competence of framework. Novelty of our work is it improved competency in feature extraction, use of context correlations increasing accuracy and use of spatio-temporal features in the proposed model framework.

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Section: Related Workmentioning

confidence: 99%

Emotion Detection using Stack Auto Encoder, Deep Learning and LSTM Model

Shah,

Rane,

Kinger

2024

IJCDS

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“…In this study, a deep learning model called VGG16 was used. The number of parameters for this model was reduced in proportion to the study's main objective, which is to reduce the training and testing time [16].…”

Section: Proposed Modelmentioning

confidence: 99%

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Abdulrahman

Baykara

2022

Applied Sciences

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The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CXRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 × 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model’s performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance.

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“…Numerous ongoing research efforts are dedicated to EEG emotion recognition, encompassing various research directions [8]. These include: (1) Time-Frequency Analysis, where the EEG signal is decomposed into different frequency bands using techniques such as wavelet transform or Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

“…(7) Artificial Neural Networks (ANNs) are trained on the EEG signal to recognize different emotional states based on the extracted features [17]. (8) Deep learning techniques exhibit promising results in EEG-based emotion recognition, with ongoing exploration to develop models capable of capturing the intricate and dynamic patterns of brain activity associated with distinct emotional states [18][19][20][21]. (9) Transfer learning allows models trained on one dataset to adapt to another dataset with minimal retraining.…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

et al. 2023

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One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address the issue of subject-specific features, a suitable approach is to employ projection dictionary learning, which enables the identification of emotion-relevant features across different subjects. To accomplish the objective of pattern representation and discrimination for subject-independent EEG emotion recognition, we utilized the fast and efficient projection dictionary pair learning (PDPL) technique. PDPL involves the joint use of a synthesis dictionary and an analysis dictionary to enhance the representation of features. Additionally, to optimize the parameters of PDPL, which depend on experience, we applied the genetic algorithm (GA) to obtain the optimal solution for the model. We validated the effectiveness of our algorithm using leave-one-subject-out cross validation on three EEG emotion databases: SEED, MPED, and GAMEEMO. Our approach outperformed traditional machine learning methods, achieving an average accuracy of 69.89% on the SEED database, 24.11% on the MPED database, 64.34% for the two-class GAMEEMO, and 49.01% for the four-class GAMEEMO. These results highlight the potential of subject-independent EEG emotion recognition algorithms in the development of intelligent systems capable of recognizing and responding to human emotions in real-world scenarios.

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A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues

Cited by 16 publications

References 53 publications

Emotion Detection using Stack Auto Encoder, Deep Learning and LSTM Model

Emotion Detection using Stack Auto Encoder, Deep Learning and LSTM Model

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

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