基于脑电图的驾驶中可扩展机器学习模型嗜睡检测性能研究
Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography
AI总结：
本文提出基于脑电图和脑机接口的智能框架，在SEED-VIG数据集上系统评估多种机器学习模型，发现随机森林在个体和可扩展场景下分别达到78%和79%的f1分数，优于支持向量机等模型，证明可扩展方法能有效检测驾驶嗜睡。
摘要：
背景/引言：驾驶员嗜睡是一个重要问题，也是交通事故的主要原因之一。认知神经科学和计算机科学的进展使得利用脑机接口（BCIs）和机器学习（ML）检测驾驶员嗜睡成为可能。然而，文献中缺乏对使用异构机器学习算法集合进行嗜睡检测性能的全面评估，并且有必要研究适用于受试者群体的可扩展机器学习模型的性能。方法：为解决这些局限性，本工作提出了一个智能框架，该框架采用脑机接口和基于脑电图（electroencephalography）的特征来检测驾驶场景中的嗜睡。使用SEED-VIG数据集评估针对单个受试者和群体的最佳性能模型。结果：结果表明，随机森林（Random Forest，RF）优于文献中使用的其他模型，例如支持向量机（Support Vector Machine，SVM），在个体模型上达到了78%的f1分数。在可扩展模型方面，RF达到了79%的f1分数，证明了这些方法的有效性。本出版物强调了探索多样化的机器学习算法集合和适用于受试者群体的可扩展方法的重要性，以改进嗜睡检测系统并最终减少由驾驶员疲劳引起的事故数量。结论：本研究获得的经验表明，不仅SVM，而且文献中尚未充分探索的其他模型也与嗜睡检测相关。此外，即使评估新受试者，可扩展方法在检测嗜睡方面也是有效的。因此，所提出的框架为使用脑机接口和机器学习检测驾驶场景中的嗜睡提供了一种新颖的方法。
英文摘要：
- Background / Introduction: Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers' drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, and it is necessary to study the performance of scalable ML models suitable for groups of subjects. - Methods: To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. - Results: Results show that Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. - Conclusions: The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated. Thus, the proposed framework presents a novel approach for detecting drowsiness in driving scenarios using BCIs and ML.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
