Chai Rifai, Ling Sai Ho, Hunter Gregory P, Nguyen Hung T
Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway NSW 2007, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1831-4. doi: 10.1109/EMBC.2012.6346307.
For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%.
对于基于脑电图（EEG）的脑机接口（BCI）应用，为实现低阻抗电接触，需要在头皮的毛发区域使用凝胶。这使得实际的BCI系统设置过程既耗时又不方便。此外，对其他皮质区域的研究对BCI发展也很有用。作为一种更便捷的替代方案，本文提出了一种基于EEG的BCI，它利用前额叶皮质非毛发区域，在三个电极位置（Fp1、Fpz和Fp2）对心理任务进行分类。所使用的相关心理任务包括心算、铃声、手指敲击和文字组合，另外还有基线和闭眼等附加任务。特征提取基于希尔伯特-黄变换（HHT）能量法，分类算法基于具有遗传算法（GA）优化的人工神经网络（ANN）。结果表明，闭眼期间的主导阿尔法波在前额叶皮质中仍能清晰检测到。五名受试者在心理任务与基线任务对比中的分类准确率平均为73%，心理任务组合对的平均准确率为72%。
