Towards predicting task performance from EEG signals
Towards predicting task performance from EEG signals
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DOI：
10.1109/bigdata.2017.8258478
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Michalis Papakostas;K. Tsiakas;Theodoros Giannakopoulos;F. Makedon
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文献类型：
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作者：
Michalis Papakostas;K. Tsiakas;Theodoros Giannakopoulos;F. Makedon
智能可穿戴设备导致对实时处理和共享大量生理数据的需求不断增加。现代人机交互 (HMI) 系统，特别是为用户培训和评估而设计的应用程序（例如教育或智能康复系统），应该能够跟踪和监控这些信号并相应地调整其参数，以最佳地满足每个人的特殊需求。为此，我们提出了一种被动脑机接口（BCI），在专为认知评估而设计的机器人辅助训练任务下使用无线非侵入式脑电图传感器。作为这项正在进行的工作的一部分，我们展示了在任务完成之前根据脑电图信号预测用户任务表现的初步结果。我们的研究结果凸显了我们假设的潜力，因为在对 69 名真实受试者进行评估时，我们实现了 74% 的最大准确率。
Smart wearable devices have lead to an increased need for processing and sharing large streams of physiological data in real-time. Modern Human-Machine Interaction (HMI) systems, especially applications designed for user training and assessment (e.g., educational or smart-rehabilitation systems), should be able to track and monitor those signals and adapt their parameters accordingly in order to optimally facilitate the special needs of each individual. Towards this end, we propose a passive Brain-Computer Interface (BCI), using a wireless non-intrusive EEG sensor under a robot assisted training task designed for cognitive assessment. As part of this ongoing work, we demonstrate our initial results on predicting user's task performance, from the EEG signals, before task completion. Our findings highlight the potentials of our hypotheses as we achieve a maximum accuracy rate equal to 74% when evaluated on 69 real subjects.