ArticleComputational intelligence and neuroscience2018
Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework.
Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Business Simulation Games Analysis Supported by Human-Computer Interfaces: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction.Bioengineering (Basel, Switzerland) · 2023Article
- Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review.Brain sciences · 2021Review
- Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review.Frontiers in systems neuroscience · 2021Review
- The Automatic Detection of Cognition Using EEG and Facial Expressions.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers.Sensors (Basel, Switzerland) · 2019Article
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
- Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions.Sensors (Basel, Switzerland) · 2019Review
- Emotion Recognition from Multiband EEG Signals Using CapsNet.Sensors (Basel, Switzerland) · 2019Article
- Deep Learning Network for Multiuser Detection in Satellite Mobile Communication System.Computational intelligence and neuroscience · 2019Article
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Authors and funding
4 authors.
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Abstract
Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed. In the framework, the member DBN-GCs are employed for extracting intermediate representations of EEG raw features from multiple domains separately, as well as mining interchannel correlation information by glia chains. Then, the higher level features describing time domain characteristics, frequency domain characteristics, and time-frequency characteristics are fused by a discriminative restricted Boltzmann machine (RBM) to implement emotion recognition task. Experiments conducted on the DEAP benchmarking dataset achieve averaged accuracy of 75.92% and 76.83% for arousal and valence states classification, respectively. The results show that the proposed framework outperforms most of the above deep classifiers. Thus, potential of the proposed framework is demonstrated.
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