ArticleSensors (Basel, Switzerland)2022
Automated Feature Extraction on AsMap for Emotion Classification Using EEG.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 71 citations in OpenAlex.
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Effect of Dual-Task Training on the Number of EEG Bands in Stroke Patients.Physiotherapy research international : the journal for researchers and clinicians in physical therapy · 2025Article
- Temporal Relation Modeling and Multimodal Adversarial Alignment Network for Pilot Workload Evaluation.IEEE journal of translational engineering in health and medicine · 2025Article
- Emotion Detection from EEG Signals Using Machine Deep Learning Models.Bioengineering (Basel, Switzerland) · 2024Article
- FC-TFS-CGRU: A Temporal-Frequency-Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture.Sensors (Basel, Switzerland) · 2024Article
- A Domain Generalization and Residual Network-Based Emotion Recognition from Physiological Signals.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- A multi-label text sentiment analysis model based on sentiment correlation modeling.Frontiers in psychology · 2024Article
- Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.Bioengineering (Basel, Switzerland) · 2023Article
- Identifying relevant asymmetry features of EEG for emotion processing.Frontiers in psychology · 2023Article
- A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of emotion by text analysis using machine learning.Frontiers in psychology · 2023Article
- Assessment of Model Accuracy in Eyes Open and Closed EEG Data: Effect of Data Pre-Processing and Validation Methods.Bioengineering (Basel, Switzerland) · 2022Article
- EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.International journal of environmental research and public health · 2022Article
- Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.Sensors (Basel, Switzerland) · 2022Article
- Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification.Brain sciences · 2022Article
- EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine.Brain sciences · 2022Article
- Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal.Sensors (Basel, Switzerland) · 2022Article
- Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection.Biosensors · 2022Article
- EEG Network Analysis in Epilepsy with Generalized Tonic-Clonic Seizures Alone.Brain sciences · 2022Article
- Activation Patterns of Functional Brain Network in Response to Action Observation-Induced and Non-Induced Motor Imagery of Swallowing: A Pilot Study.Brain sciences · 2022Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed. The asymmetry in different brain regions is captured in a 2D vector, termed the AsMap, from the differential entropy features of EEG signals. These AsMaps are then used to extract features automatically using a convolutional neural network model. The proposed feature extraction method has been compared with differential entropy and other feature extraction methods such as relative asymmetry, differential asymmetry and differential caudality. Experiments are conducted using the SJTU emotion EEG dataset and the DEAP dataset on different classification problems based on the number of classes. Results obtained indicate that the proposed method of feature extraction results in higher classification accuracy, outperforming the other feature extraction methods. The highest classification accuracy of 97.10% is achieved on a three-class classification problem using the SJTU emotion EEG dataset. Further, this work has also assessed the impact of window size on classification accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.