Machine Empathy: Digitizing Human Emotions

Roshdy, Ahmed; Kork, Samer Al; Karar, Abdullah S.; Sabi, Ahmed Al; Barakeh, Zaher Al; Elsayed, Fahmi; Beyrouthy, T.; Naït‐Ali, Amine

doi:10.1145/3459104.3459154

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…The use of deep learning and machine learning techniques to identify emotional states from EEG data has been the focus of recent research [21,22]. These methods, like other machine learning methods, start by preprocessing the EEG data in order to remove noise.…”

Section: Emotion Recognition Through Eeg Signalsmentioning

confidence: 99%

“…One of the key benefits of EEG-based emotion recognition is its ability to track emotional responses in real-time. This opens up potential applications in fields such as neuromarketing, human-machine interaction, and affective computing [17,21,35]. The process typically involves the preprocessing of raw data, extracting features, and classifying emotions, as illustrated in Figure 2.…”

Section: Emotion Recognition Through Eeg Signalsmentioning

confidence: 99%

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

Roshdy,

Karar,

Kork

et al. 2024

Applied Sciences

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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.

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Section: Emotion Recognition Through Eeg Signalsmentioning

confidence: 99%

Section: Emotion Recognition Through Eeg Signalsmentioning

confidence: 99%

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

Roshdy,

Karar,

Kork

et al. 2024

Applied Sciences

Self Cite

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“…Another example is Handtrack, an end-to-end trainable CNN model that learns to track the human hand [16,151]. Also, studies have been made in the aim of modeling and digitizing human emotions, which can have projections on social robot behavior and intelligence perception [152,153].…”

Section: Artificial Intelligencementioning

confidence: 99%

A Survey on Recent Advances in Social Robotics

et al. 2022

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Over decades, social robotics has evolved as a concept that presently covers different areas of application, and interacts with different domains in technology, education, medicine and others. Today, it is possible to envision social robots in tasks that were not expected years ago, and that is not only due to the evolution of social robots, but also to the evolution of the vision humans have for them. This survey addresses recent advances in social robotics from different perspectives. Different contexts and areas of application of social robots are addressed, as well as modalities of interaction with humans. Different robotic platforms used in social contexts are shown and discussed. Relationships of social robotics with advances in other technological areas are surveyed, and methods and metrics used for the human evaluation of the interaction with robots are presented. The future of social robotics is also envisioned based on surveyed works and from different points of view.

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“…This is done through computer vision coupled with artificial intelligence (AI) and machine learning solutions. Among all AI approaches, deep learning has demonstrated the most promising potential and has been widely used to study human emotions [2][3][4][5]. Most deep learning models are trained on large-scale data sets of labeled facial images [3,6,7], brain maps [5,8], audio waves [9], and text [10,11], and use the extracted features to classify emotions with high accuracy, outperforming traditional methods.…”

Section: Introductionmentioning

confidence: 99%

“…Among all AI approaches, deep learning has demonstrated the most promising potential and has been widely used to study human emotions [2][3][4][5]. Most deep learning models are trained on large-scale data sets of labeled facial images [3,6,7], brain maps [5,8], audio waves [9], and text [10,11], and use the extracted features to classify emotions with high accuracy, outperforming traditional methods. In addition, researchers now investigate multimodal approaches, which combine two or more inputs such as visual and textual data to learn more accurate emotion representations [11,12].…”

Section: Introductionmentioning

confidence: 99%

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

et al. 2023

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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.

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Machine Empathy: Digitizing Human Emotions

Cited by 6 publications

References 13 publications

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

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

A Survey on Recent Advances in Social Robotics

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

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