A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

Apicella, Andrea; Arpaïa, Pasquale; Isgrò, Francesco; Mastrati, Giovanna; Moccaldi, Nicola

doi:10.1109/access.2022.3219844

Cited by 21 publications

(8 citation statements)

References 161 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Daoud and Bayoumi [ 33 ] used transfer learning and semi-supervised approaches to identify relevant channels. According to previous research [ 34 , 35 ], the most commonly used electrodes for emotion recognition are O1, O2, FP1, FP2, F3, F4, C3, C4, AF3, AF4, FC5, FC6, T7, T8, F7, F8, P7, and P8. Moreover, the frontal lobe electrodes like F3, F4, and AF3/4 have been used in more than 70% of the studies, which shows that many researchers believe the frontal cortex plays an important role in emotion processing.…”

Section: Methodsmentioning

confidence: 99%

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Aldawsari,

Al-Ahmadi,

Muhammad

2023

Diagnostics

View full text Add to dashboard Cite

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Aldawsari,

Al-Ahmadi,

Muhammad

2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Another crucial step in simplifying the entire emotion identification system is determining the quantity of electrodes used to generate the heat maps and its placement locations on the human scalp. Studies frequently use the method of focusing on certain brain regions, such as the amygdala and prefrontal cortex, which are known to be involved in emotional processing [21,38,56]. Optimum channel selection can help the system identify emotions more accurately by cutting down on unwanted noise and irrelevant inputs.…”

Section: Eeg-based Emotion Recognitionmentioning

confidence: 99%

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

Roshdy,

Karar,

Kork

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This paper addresses the limitations of relying solely on facial expressions for emotion recognition by proposing an advanced approach that emphasizes continuous monitoring of electroencephalography (EEG) signals. Recognizing the potential for deception in facial expressions, our study leverages the growing interest in EEG signals, tapping into advancements in deep learning and machine learning. By optimizing the configuration of EEG electrodes, our approach enhances the accuracy of emotion classification systems, offering a streamlined solution. The proposed multi-input system refines EEG-based emotion recognition efficiency and integrates facial expression analysis to enhance overall system effectiveness. Through the application of brain heat map topographies and facial expression recognition, our system, employing just nine electrodes, outperforms basic emotion recognition setups. Experimental results validate that combining facial expression analysis with EEG signals provides a more comprehensive and accurate understanding of human emotions. This innovative approach holds significance across various sectors, including healthcare, psychology, and human–computer interaction. The paper introduces a novel multi-input system approach, collaboratively fusing two powerful deep learning algorithms: two Convolutional Neural Networks (CNNs). The proposed EEG-based CNN algorithm achieves an efficiency of 87.43%, rising to 91.21% when integrated with the DeepFace CNN. The seamless integration of facial expressions and brain topographies enables the system to efficiently harness abundant information from both modalities, ensuring a thorough comprehension of human emotions. By capitalizing on the combined advantages of analyzing facial expressions and EEG-derived brain topography, this avant-garde technique substantially improves both precision and efficiency in emotion recognition systems. This enhancement establishes a foundation for the introduction of innovative applications across a spectrum of fields.

show abstract

“…Researchers in the disciplines of affective computing, human-computer interaction, and psychology can benefit from the database because it is freely accessible and can be downloaded from the official website. The optimization of the EEG channel selection for emotion recognition can be classified into two broad categories [46]: (i) prior knowledge-based approach with repeated experimentation, and (ii) data-driven approaches. Only a few studies exist using the latter approach while adopting the DEAP data set, which ensures repeatability and crossverification.…”

Section: Eeg-based Signal Extraction For Emotion Recognitionmentioning

confidence: 99%

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

View full text Add to dashboard Cite

Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing.

show abstract

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

Cited by 21 publications

References 161 publications

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

Contact Info

Product

Resources

About