ArticleCognitive neurodynamics2019
A hybrid model for EEG-based gender recognition.
Article in Cognitive neurodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions.Entropy (Basel, Switzerland) · 2024Article
- Decoding motor imagery with a simplified distributed dipoles model at source level.Cognitive neurodynamics · 2023Article
- Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.Entropy (Basel, Switzerland) · 2022Article
- An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs.Medical & biological engineering & computing · 2022Article
- Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task.Entropy (Basel, Switzerland) · 2021Article
- Review on EEG-Based Authentication Technology.Computational intelligence and neuroscience · 2021Review
- Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs.Journal of healthcare engineering · 2021Article
- A novel facial attractiveness evaluation system based on face shape, facial structure features and skin.Cognitive neurodynamics · 2020Article
- Removing uncertainty in neural networks.Cognitive neurodynamics · 2020Article
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Authors and funding
2 authors.
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Abstract
The gender recognition is an important research field to study evidence regarding some personal characteristics in the information and data society. However, some current traditional methods such as vision and sound have been exposed their own security weaknesses. Recently, biometric gender recognition based on Electroencephalography (EEG) signals has been widely used in information safety and medical fields. It is necessary to explore potential of using EEG to present a more robust and accurate result with larger training data based on sophisticated machine learning approaches. In this contribution, we present an automated gender recognition system by a hybrid model based on EEG data of resting state from twenty-eight subjects. These data are useful and handy to get insights into assessing the differences in personal gender. For achieving a good performance and a strong robustness, the system develops a hybrid model of combining random forest and logistic regression, and employs four common entropy measures to analyze the non-stationary EEG signals. Result also suggests that the recognition performance achieve an improved progress with an accuracy of 0.9982 and AUC of 0.9926 based on a nested tenfold cross-validation loop, implying that show a significant potential applicability of the proposed approach and is capable of recognizing personal gender.
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Registered trials
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.