A Class-Incremental Learning Method Based on Preserving the Learned Feature Space for EEG-Based Emotion Recognition

Jiménez-Guarneros, Magdiel; Alejo, R.

doi:10.3390/math10040598

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…Accuracy valence arousal Tugba ERGIN and Mehmet Akif OZDEMIR [18] 67.93% 71.60% Yuzhe Zhang, Huan Liu et al [19] 72.89% 77.03% Rabiul Islam, Milon Islam et al [20] 78.22% 74.92% Ante Topic, Mladen Russo [21] 74.91% 75.44% Qi Li, Yunqing Liu et al [22] 75.9% 79.3% Magdiel Jiménez-Guarneros and Roberto Alejo-Eleuterio et al [23] Avg:79.36 Yue Gao, Xiangling Fu et al [24] 81.77% 81.95% McM 85.23% 85.50%…”

Section: Methodsmentioning

confidence: 99%

EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks

Chao,

Yuan

2023

EAI Endorsed Trans e-Learn

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A multi-view self-attention module is proposed and paired with a multi-scale convolutional model to builda multi-view self-attention convolutional network for multi-channel EEG emotion recognition. First, timeand frequency domain characteristics are extracted from multi-channel EEG signals, and a three-dimensionalfeature matrix is built using spatial mapping connections. Then, a multi-scale convolutional network extractsthe high-level abstract features from the feature matrix, and a multi-view self-attention network strengthensthe features. Finally, use the multilayer perceptron for sentiment classification. The experimental results revealthat the multi-view self-attention convolutional network can effectively integrate the time domain, frequencydomain, and spatial domain elements of EEG signals using the DEAP public emotion dataset. The multi-viewself-attention module can eliminate superfluous data, apply attention weight to the network to hasten networkconvergence, and enhance model recognition precision.

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Section: Methodsmentioning

confidence: 99%

EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks

Chao,

Yuan

2023

EAI Endorsed Trans e-Learn

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“…Magdiel J.G. et al applied a class incremental learning approach to EEG-based emotion recognition [24].…”

Section: Incremental Learning Methodsmentioning

confidence: 99%

Using Domain Adaptation for Incremental SVM Classification of Drift Data

Tang

Lin

2022

Mathematics

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A common assumption in machine learning is that training data is complete, and the data distribution is fixed. However, in many practical applications, this assumption does not hold. Incremental learning was proposed to compensate for this problem. Common approaches include retraining models and incremental learning to compensate for the shortage of training data. Retraining models is time-consuming and computationally expensive, while incremental learning can save time and computational costs. However, the concept drift may affect the performance. Two crucial issues should be considered to address concept drift in incremental learning: gaining new knowledge without forgetting previously acquired knowledge and forgetting obsolete information without corrupting valid information. This paper proposes an incremental support vector machine learning approach with domain adaptation, considering both crucial issues. Firstly, a small amount of new data is used to fine-tune the previous model to generate a model that is sensitive to the new data but retains the previous data information by transferring parameters. Secondly, an ensemble and model selection mechanism based on Bayesian theory is proposed to keep the valid information. The computational experiments indicate that the performance of the proposed model improved as new data was acquired. In addition, the influence of the degree of data drift on the algorithm is also explored. A gain in performance on four out of five industrial datasets and four synthetic datasets has been demonstrated over the support vector machine and incremental support vector machine algorithms.

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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 Class-Incremental Learning Method Based on Preserving the Learned Feature Space for EEG-Based Emotion Recognition

Cited by 5 publications

References 59 publications

EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks

EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks

Using Domain Adaptation for Incremental SVM Classification of Drift Data

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

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