Lin Chin-Teng, Wang Yu-Kai, Fang Chieh-Ning, Yu Yi-Hsin, King Jung-Tai
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6642-5. doi: 10.1109/EMBC.2015.7319916.
The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.
脑成像技术的进步为开发和研究脑机接口（BCI）带来了机遇，脑机接口是一种与计算机和环境进行交互的方式。所测量的脑活动通常由感兴趣的信号和噪声组成。应用便携式设备并去除噪声对实际应用中的脑机接口有益。在本研究中，一个便携式脑电图（EEG）系统通过无线传输非侵入性地获取脑动态，同时六名受试者参与了快速序列视觉呈现（RSVP）范式。传统上通过总体平均（EA）来估计事件相关电位（ERP）以提高信噪比。还使用了一种数据重用径向基函数网络（DR-RBFN）的自适应滤波器作为估计器。结果表明，该便携式EEG系统能够稳定地获取脑活动。此外，通过DR-RBFN可以从有限的EEG数据样本中清晰地探索与任务相关的电位。根据便携式设备采集的无伪迹数据，本研究证明了在不久的将来将脑机接口从实验室研究推向实际应用的潜力。
