使用自动生成的标签实现基于深度学习的EEG电极检测
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
摘要：
脑电图（EEG）允许对脑电活动进行源测量。特别是对于反向定位，需要知道头皮上的电极位置。通常，诸如光学数字化扫描仪之类的系统用于使用触针进行精确定位。然而，该方法是耗时的，因为每个电极需要手动扫描，并且扫描系统是昂贵的。我们建议使用RGBD相机使用深度学习方法直接跟踪图像中的电极。研究和评估深度学习方法需要大量的标记数据。为了克服耗时的数据注释，我们使用机器人设置生成大量的地面实况标签。我们证明了基于深度学习的电极检测是可行的，平均绝对误差为5.69 +-6.1 mm，并且我们的注释方案为研究电极检测的深度学习方法提供了有用的环境。
英文摘要：
Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 +- 6.1mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.
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