推进被动式生物识别:基于连续在线脑电图的机器错误检测中两种时间导数特征和基于效应大小的特征选择的可行性研究

Yanzhao Pan, Thorsten O. Zander, Marius Klug
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引用次数: 0

摘要

脑机接口(BCI)在人机协作中的新兴集成为动态自适应交互带来了希望。在辅助设备中使用脑电图(EEG)测量的错误相关电位(ErrPs)进行在线错误检测,为提高此类设备的可靠性提供了一种实用方法。然而,连续在线错误检测面临着各种挑战,如开发高效、轻便的分类技术以实现快速预测,减少伪影造成的误报,以及处理脑电信号的非稳态性等。进一步的研究对于解决在线会话中连续分类的复杂性至关重要。通过这项研究,我们展示了一种基于连续在线脑电图的机器错误检测综合方法,该方法在第 32 届国际人工智能联合会议的竞赛中脱颖而出。比赛包括两个阶段:使用预先录制的标记脑电图数据进行模型开发的离线阶段,以及离线阶段 3 个月后的在线阶段,在在线阶段,这些模型在连续流脑电图数据上进行现场测试,以实时检测矫形器运动中的错误。我们的方法将两个时间派生特征与基于效应大小的特征选择技术结合起来进行模型训练,同时采用轻量级噪声过滤方法进行在线会话,无需重新校准模型。在离线阶段训练的模型不仅在所有参与者中取得了 89.9% 的高平均交叉验证准确率,而且在初始数据收集 3 个月后的在线会话中也表现出色,无需进一步校准,保持了 1.7% 的低总体误报率和快速反应能力。我们的研究为该领域做出了两项重大贡献。首先,它证明了将两个时间导数特征与基于效应大小的特征选择策略相结合的可行性,尤其是在基于脑电图的在线 BCI 中。其次,我们的研究引入了一种创新方法,该方法设计用于连续在线误差预测,其中包括一种直接的噪声抑制技术,以减少误报。这项研究是对无缝错误检测方法的可行性调查,有望改变神经自适应技术和人机交互领域的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection
The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.
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