Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Xie, Oiao; Liu, Zhentao; Ding, Xuewen

doi:10.23919/chicc.2018.8483496

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…This study used SVM, random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. XGBoost is a method to increase the reliability of the base model while maximizing its stability [40]. Lin et al (2018) [41] also reported that the accuracy was improved compared to a single predictive model when applying XGBoost to a stacking ensemble model.…”

Section: Meta Model: Xgboostmentioning

confidence: 99%

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study

Byeon

2021

IJERPH

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This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived from final machine learning models with the best performance. This study analyzed 1558 elderly (695 males and 863 females) who were 60 years or older and completed the Zung’s Self-Rating Anxiety Scale (SAS). We used support vector machine (SVM), random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. The analysis results confirmed that the predictive performance of the “SVM + Random forest + LightGBM + AdaBoost + XGBoost model (stacking ensemble: accuracy 87.4%, precision 85.1%, recall 87.4%, and F1-score 85.5%)” was the best. Also, the results of this study showed that the elderly who often (or mostly) felt subjective loneliness, had a Self Esteem Scale score of 26 or less, and had a subjective communication with their family of 4 or less (on a 10-point scale) were the group with the highest risk anxiety disorder. The results of this study imply that it is necessary to establish a community-based mental health policy that can identify elderly groups with high anxiety risks based on multiple risk factors and manage them constantly.

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Section: Meta Model: Xgboostmentioning

confidence: 99%

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study

Byeon

2021

IJERPH

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“…Based on the binary classification task of EEG signals researched in the proposed method, the schematic diagram of the LDA algorithm for binary classification is shown in Figure 4, and the specific implementation is as follows. e process of the LDA algorithm [39] is described as follows.…”

Section: Feature Selectionmentioning

confidence: 99%

Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Chen

Chang

Guo

2021

Mathematical Problems in Engineering

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In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.

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“…Wang and Yue [21] pointed out that in specific problems and scenarios, combining the advantages of multiple models, the classification results may be better. Xie et al [22] proposed a high-precision EEG sentiment recognition model by fusing XGBoost, LightGBM, and random forest.…”

Section: Introductionmentioning

confidence: 99%

Bullet Subtitle Sentiment Classification Based on Affective Computing and Ensemble Learning

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some traditional affective computing methods only consider the text information and ignore the information of other dimensions, we construct a bullet subtitle affective computing method by combining the information of other dimensions of the bullet subtitle. Finally, aiming at the problem that existing classification algorithms ignore the importance of sentiment words in short texts, we propose a sentiment classification method based on affective computing and ensemble learning. Our experiment results show that the proposed method has higher accuracy and better practical application effect.

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Electroencephalogram Emotion Recognition Based on A Stacking Classification Model

Cited by 14 publications

References 15 publications

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study

Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Bullet Subtitle Sentiment Classification Based on Affective Computing and Ensemble Learning

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