ArticleBrain sciences2019
EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model.
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.Scientific reports · 2024Article
- Investigation of different ML approaches in classification of emotions induced by acute stress.Heliyon · 2024Article
- A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.Frontiers in neuroscience · 2023Article
- DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing.Frontiers in human neuroscience · 2023Article
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.Cognitive neurodynamics · 2022Article
- EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network.Brain sciences · 2022Article
- Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm.Scientific reports · 2022Article
- An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data.Frontiers in robotics and AI · 2022Article
- EEG-based detection of emotional valence towards a reproducible measurement of emotions.Scientific reports · 2021Article
- A Study on Seizure Detection of EEG Signals Represented in 2D.Sensors (Basel, Switzerland) · 2021Article
- InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.Sensors (Basel, Switzerland) · 2020Article
- EEG-based emotion recognition using 4D convolutional recurrent neural network.Cognitive neurodynamics · 2020Article
- Discovering hidden information in biosignals from patients using artificial intelligence.Korean journal of anesthesiology · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.
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Registered trials
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