EEG signals are vulnerable to several noise and artifacts occurred by muscle activities and body movements. Reducing these artifacts has been a challenge issue to design and develop a reliable mobile EEG system for various real-life applications including home entertainment as well as clinical monitoring, assessment and rehabilitation. In this paper, we describe a method for removing motion artifacts occurred by body movement using inertial sensors. The key contribution of this work is the automatic identification of independent components representing motion artifacts from EEG signals, incurring minimal computation in real-time. The experimental results from the application of the method show that it is able to remove, in real-time, the motion noise of body movement in an real-world environment with improving the quality of EEG signals up to 82% compared with recorded in seated condition.

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Animals are capable of using visual cues to find the correct route during navigation. These visual cues, which contain spatial information on the direction towards the goal point, are perceived either allocentrically or egocentrically. In this study, we examined how navigating with these two types of visual cues affects the learning processes of rodents. To present egocentrically-stable spatial cues, we devised a head-mounted device that provided discriminative orientation cues that indicated the correct choice at a fork within a double Y-maze. For allocentrically-stable spatial cues, LEDs serving as external route-mark cues were attached to the walls of the double Y-maze and illuminated to indicate the correct pathway. To rule out the possibility of the mice using extra-maze cues, we rotated the entire maze and used different start and goal sites for every trial. Our results revealed that mice using egocentric cues and external route-mark cues both showed a sigmoidal learning process for spatial navigation and that external route mark-based learning, surprisingly, learned faster than egocentric stimulus-based learning in egocentric space.