Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

Zhang, Jian; Min, Yihou

doi:10.5755/j01.itc.49.3.23948

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…DEAP and DREAMER datasets have been widely used in EEG-based emotion recognition research [ 16 , 42 , 43 , 44 , 45 ]. Therefore, we validated our proposed FCAN–XGBoost algorithm on the DEAP and DREAMER datasets.…”

Section: Methodsmentioning

confidence: 99%

“…The selection of feature extraction methods and classification algorithms plays a pivotal role in the outcome of the EEG emotion recognition task [ 15 ]. Over the past few years, researchers have conducted extensive investigations into the selection of appropriate EEG feature extraction methods and classification algorithms [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Such efforts have yielded significant advancements in this field.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to the research focusing solely on binary emotions, multi-classification research on emotions has promising prospects [ 42 , 43 , 44 , 45 ]. Hu et al [ 46 ] introduced a hybrid model comprised of a CNN, a bidirectional long short-term memory network (BiLSTM), and a multi-head self-attention mechanism (MHSA) which transforms EEG signals into temporal frequency maps for emotion classification.…”

Section: Related Workmentioning

confidence: 99%

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FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

Zong

Xiong

Zhou

et al. 2023

Sensors

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In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

Zong

Xiong

Zhou

et al. 2023

Sensors

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“…Therefore, these basis functions can well reflect the characteristics of the original data. After the speech signal with non-linear and non-stationary characteristics is decomposed by empirical mode, some finite simple signal components can be obtained, which are called intrinsic mode components (Zhang and Min, 2020). The screening algorithm is the core process in empirical mode decomposition.…”

Section: Hht Theorymentioning

confidence: 99%

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Lin

2022

Front. Psychol.

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With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher.

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“…Extract features from these five frequency bands. These features usually include power spectral density (PSD) [ 11 ], differential entropy (DE) [ 12 , 13 ], differential asymmetry (DASM) [ 14 ], and rational asymmetry (RASM) [ 15 , 16 ]. EEG signal is a non-stationary and non-linear random signal [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals

Liu

et al. 2022

Entropy

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As a major daily task for the popularization of artificial intelligence technology, more and more attention has been paid to the scientific research of mental state electroencephalogram (EEG) in recent years. To retain the spatial information of EEG signals and fully mine the EEG timing-related information, this paper proposes a novel EEG emotion recognition method. First, to obtain the frequency, spatial, and temporal information of multichannel EEG signals more comprehensively, we choose the multidimensional feature structure as the input of the artificial neural network. Then, a neural network model based on depthwise separable convolution is proposed, extracting the input structure’s frequency and spatial features. The network can effectively reduce the computational parameters. Finally, we modeled using the ordered neuronal long short-term memory (ON-LSTM) network, which can automatically learn hierarchical information to extract deep emotional features hidden in EEG time series. The experimental results show that the proposed model can reasonably learn the correlation and temporal dimension information content between EEG multi-channel and improve emotion classification performance. We performed the experimental validation of this paper in two publicly available EEG emotional datasets. In the experiments on the DEAP dataset (a dataset for emotion analysis using EEG, physiological, and video signals), the mean accuracy of emotion recognition for arousal and valence is 95.02% and 94.61%, respectively. In the experiments on the SEED dataset (a dataset collection for various purposes using EEG signals), the average accuracy of emotion recognition is 95.49%.

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Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

Cited by 9 publications

References 34 publications

FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology

Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals

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