持续视觉注意力训练平台中的精度增强：用于分类器微调的离线EEG信号分析
Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning
AI总结：
本研究开发开源BCI平台，通过ERP、希尔伯特和小波变换提取EEG特征，构建个性化SVM和RF模型解码持续视觉注意力，SVM准确率达80%，为注意力评估和闭环脑波调节提供基础。
摘要：
本研究开发了一个新型开源脑机接口（BCI）平台，用于解码与持续注意力相关的头皮脑电图（EEG）信号。EEG信号采集使用无线头戴设备，在持续视觉注意力任务中进行。任务要求参与者区分叠加了场景和人脸的复合图像，仅对相关子类别作出响应，同时忽略不相关子类别。七名志愿者参与了该实验。采集到的数据通过事件相关电位（ERP）、希尔伯特变换和小波变换进行分析，以提取时域和频域特征。针对每位参与者，利用其提取的特征，开发了具有调优超参数的个性化支持向量机（SVM）和随机森林（RF）模型。这些模型旨在解码参与者对人和场景刺激的注意力状态。SVM模型取得了更高的平均准确率80%和曲线下面积（AUC）0.86，而RF模型的平均准确率为78%，AUC为0.8。该工作表明其在视觉注意力评估以及未来闭环脑波调节系统开发中的潜在应用价值。
英文摘要：
In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80\% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78\% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.
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。
