Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal

Article Biology

A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition

Rui Li et al.

Summary: In this study, a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition was proposed. The experimental results show that the proposed method achieves better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22% on the DEAP database, and the average accuracy on the SEED database is 84.44%.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Article Computer Science, Artificial Intelligence

Artificial Neural Network training using metaheuristics for medical data classification: An experimental study

Tapas Si et al.

Summary: This study investigates the performance of different metaheuristics as learning algorithms for training Artificial Neural Networks (ANN) in medical data classification tasks. The results establish that the Equilibrium Optimizer algorithm outperforms other algorithms in terms of performance.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Computer Science, Artificial Intelligence

Ensemble of feature selection algorithms: a multi-criteria decision-making approach

Amin Hashemi et al.

Summary: In this paper, ensemble feature selection is modeled as a Multi-Criteria Decision-Making (MCDM) process, and a novel method called EFS-MCDM is proposed to rank and score features. Experimental results demonstrate that the proposed method outperforms other similar methods in terms of accuracy and efficiency.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2022)

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Article Computer Science, Artificial Intelligence

A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system

Aditi Sakalle et al.

Summary: The research aims to develop a brainwave-driven system for recognizing positive, negative, and neutral emotions, with a focus on the LSTM model. The classification performance is evaluated through comparisons with different models and cross-validation techniques, with results showing that brain signals from the 18-25 age group are most effective in emotion identification, and females are more emotionally active than males.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Emotion Recognition Using Portable EEG Device

Aditi Sakalle et al.

Summary: Emotions are an integral part of human behavior, classified into positive and negative categories. This study proposes a method for emotion classification using a portable EEG device, with experimental results showing high accuracy in emotion recognition.

ARTIFICIAL INTELLIGENCE AND SUSTAINABLE COMPUTING FOR SMART CITY, AIS2C2 2021 (2021)

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Article Clinical Neurology