A feature‐based on potential and differential entropy information for electroencephalogram emotion recognition

Li, Dongdong; Xie, Li; Chai, Bing; Wang, Zhe

doi:10.1049/ell2.12388

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…To better analyze the content of work orders, new words such as network words, community names and department abbreviations that had appeared in the text should be extracted, segmented and analyzed to improve the accuracy of the content analysis of texts. Therefore, this paper adopts the left and right information entropy method (Li et al .,2022) to discover new words in texts. One of the conditions for considering two fragments as a word is that the word will be mentioned in many contexts.…”

Section: Research Framework For Text Processing and Numerical Data De...mentioning

confidence: 99%

“…where A and B are the prefix and suffix sets of candidates W, respectively. The larger the left and right information entropy is, the richer the semantics of the word would be, the greater the degree of freedom of the word and the more likely it is to become an independent new word (Li et al .,2022).…”

Section: Research Framework For Text Processing and Numerical Data De...mentioning

confidence: 99%

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Understanding the relationship between normative records of appeals and government hotline order dispatching: a data analysis method

Zhang

2024

DTA

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PurposeAdvanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.Design/methodology/approachIn this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.FindingsThe complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.Originality/valueThe proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

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Section: Research Framework For Text Processing and Numerical Data De...mentioning

confidence: 99%

Section: Research Framework For Text Processing and Numerical Data De...mentioning

confidence: 99%

Understanding the relationship between normative records of appeals and government hotline order dispatching: a data analysis method

Zhang

2024

DTA

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“…Experimental works produced an 85.01% accuracy score for the classification of the four emotions. The Differential Entropy (DE) method is frequently preferred in EEG emotion recognition [ 33 , 34 , 35 , 36 ]. In their study [ 37 ], Zheng et al applied six different feature extraction methods (PSD, DE, Differential Asymmetry (DASM), Rational Asymmetry, Asymmetry (RASM), Differential Caudality) for emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

et al. 2022

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Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition.

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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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A feature‐based on potential and differential entropy information for electroencephalogram emotion recognition

Cited by 9 publications

References 21 publications

Understanding the relationship between normative records of appeals and government hotline order dispatching: a data analysis method

Understanding the relationship between normative records of appeals and government hotline order dispatching: a data analysis method

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

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

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