۱- Introduction

۲- System architecture and design

۳- Methods

۴- Experimental results

۵- Discussions

۶- Conclusions

References

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.

Introduction

Electroencephalograph (EEG)-based brain machine interface is a system which can translate the mental tasks of the user into a command to communicate with the external device without using muscle [1,2]. Most of BMIs require many EEG channels to acquire EEG signals from multiple sites on the scalp skin to provide a good performance. Before measuring multi-channel brain responses, a prolonged preparation time is required and it directly affects the convenience in use. Moreover, these EEG channels may contain many irrelevant signals, Therefore, for the development of brain machine interfaces (BMIs), selecting the optimal subset of the EEG channels to replace the use of all EEG channels is an important issue.