Design of smart EEG cap. (September 2019) - Lin, Bor-Shing; Huang, Yao-Kuang; Lin, Bor-Shyh
Highlights: A smart EEG cap was proposed to select the meaningful EEG channels and enhance EEG features within a local spatial distribution. Dry active electrodes with spring loaded were also implemented and applied in the smart EEG cap, to measure EEG in hairy site, without conductive gels. An analog spatial filter circuit was also designed to perform the function of spatial filtering without any calculation. The proposed smart EEG cap could perform the function of selecting channel in the front-end device to only transmit the EEG signals of the selected channels. The proposed smart EEG cap was also applied in the design of the motion-imagery BMI. The experimental results showed that the proposed smart EEG cap exactly provided a good performance of detecting motor imagery. Abstract: Background and Objective: Brain machine interface (BMI) is a system which communicates the brain with the external machines. In general, an electroencephalograph (EEG) machine has to be used to monitor multi-channel brain responses to improve the BMI performance. However, the bulky size of the EEG machine and applying conductive gels in EEG electrodes also cause the inconvenience of daily life applications. How to select the relevant EEG channel and remove irrelevant channels is important and useful for the development of BMIs. Methods: In this research, a smart EEG cap was proposed to improve the above issues. Different from the conventional EEG machine, the proposed smart EEG cap contain a spatial filtering circuit to enhance EEG features in local area, and it could also select the relevant EEG channel automatically. Moreover, the novel dry active electrodes were also designed to acquire EEG without conductive gels in the hairy skin of the head, to improve the convenience in use. Results: Finally, the proposed smart EEG cap was applied in motion imagery-based BMI and several experiments were tested to valid the system performance. The proposed smart EEG cap could effectively enhance EEG features and select relevant EEG channel, and the information transfer rate of BMI was about 6.06 bits/min. Conclusions: The proposed smart EEG cap has advantages of measuring EEG without conductive gels and wireless transmission to effectively improve the convenience of use, and reduce the limitation of activity in daily life. In the future, it might be widely applied in other BMI applications.