An Empirical Evaluation of Methodologies Used for Emotion Recognition via EEG Signals

Hinvest, Neal; Ashwin, Chris; Carter, Felix; Hook, James; Smith, Laura G. E.; Stothart, George

doi:10.1080/17470919.2022.2029558

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…For emotion recognition methods, there are mainly scale methods [13,14], machine learning [15][16][17], and deep learning algorithms [18][19][20]. e used recognition data are electroencephalogram (EEG) [21], electrocardiogram (ECG) [22], voice [23], video [24], expression [25], action [26], and so on. For the application field of this paper, athletes can collect audio, video, EEG, ECG, and other data when exercising.…”

Section: Introductionmentioning

confidence: 99%

A Recognition Method of Athletes’ Mental State in Sports Training Based on Support Vector Machine Model

2022

Journal of Electrical and Computer Engineering

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Athletes participate in competitive competitions, and the ultimate goal is to better display their personal competitive level in the competition so as to achieve the goal of defeating their opponents and winning the competition. In all types of competitions, most matches are instantaneous, and opportunities are fleeting. The instantaneous nature and fierce competition of sports competitions require athletes who participate in sports competitions to have a high psychological quality. It can be seen that the quality of the mental state directly determines the performance of the athletes in usual training and competition. In the process of sports, if athletes can obtain real-time changes in their mental states when they encounter various situations, they can formulate more targeted and effective training or competition strategies according to the athletes’ states. For the opponent, by analyzing the opponent’s psychological state during exercise, the game strategy can be adjusted in real time in a targeted manner, and the probability of winning the game can be provided. Based on this background, this paper proposes to use support vector machine (SVM) to identify the mental state of athletes during exercise. This paper first collects the data of body movements and facial expressions of athletes during training or competition. Use multimodal data to train an SVM model. Output the emotional state of athletes at different stages based on test data. In order to verify the applicability of the method in this paper to the athlete subjects, several comparative models were used in the experiment to verify the performance of the used models. The experimental results show that the accuracy rate of emotion recognition obtained by this method is more than 80%. This shows that the research in this paper has certain application value.

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Section: Introductionmentioning

confidence: 99%

A Recognition Method of Athletes’ Mental State in Sports Training Based on Support Vector Machine Model

2022

Journal of Electrical and Computer Engineering

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Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

Samal,

Hashmi

2024

Artif Intell Rev

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Emotion is a subjective psychophysiological reaction coming from external stimuli which impacts every aspect of our daily lives. Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models is also given. We discuss several EEG feature extractions, feature selection/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the applications, challenges and potential areas for future research in identification and classification of human emotional states.

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What do you have in mind? ERP markers of visual and auditory imagery

Proverbio

Tacchini

Jiang

2023

Brain and Cognition

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An Empirical Evaluation of Methodologies Used for Emotion Recognition via EEG Signals

Cited by 5 publications

References 26 publications

A Recognition Method of Athletes’ Mental State in Sports Training Based on Support Vector Machine Model

A Recognition Method of Athletes’ Mental State in Sports Training Based on Support Vector Machine Model

Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review

What do you have in mind? ERP markers of visual and auditory imagery

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