ArticleComputational intelligence and neuroscience2019
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.
Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 130 citations in OpenAlex.
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
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- Predict the writer's trait emotional intelligence from reproduced calligraphy.Scientific reports · 2025Article
- Assessing cross cultural urban park emotional wellbeing impact in Shanghai and London.Scientific reports · 2025Article
- Assessment of flight fatigue using heart rate variability and machine learning approaches.Frontiers in neuroscience · 2025Article
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- The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system.Cognitive neurodynamics · 2023Article
- Blood pressure stratification using photoplethysmography and light gradient boosting machine.Frontiers in physiology · 2023Article
- OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.Frontiers in neurology · 2023Article
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Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).
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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.