ArticleBrain sciences2020
Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A multimodal EEG-based psychological risk representation method using spatiotemporal graph attention and adaptive gated fusion.Frontiers in psychiatry · 2026Article
- Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network.Brain sciences · 2023Article
- An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.Sensors (Basel, Switzerland) · 2022Article
- [Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Stronger connectivity and higher extraversion protect against stress-related deterioration of cognitive functions.Scientific reports · 2021Article
- A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry.Sensors (Basel, Switzerland) · 2020Article
- Brain Activity during Different Throwing Games: EEG Exploratory Study.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personality is the characteristic set of an individual's behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human-computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.
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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.