EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.

Electroencephalography (EEG) is often used in source analysis studies, in which the locations of cortex regions responsible for a signal are determined. For this to be possible, accurate positions of the electrodes at the scalp surface must be determined, otherwise errors in the source estimation will occur. Today, several methods for acquiring these positions exist but they are often not satisfyingly accurate or take a long time to perform. Therefore, in this paper we describe a method capable of determining the positions accurately and fast. This method uses an infrared light motion capture system (IR-MOCAP) with 8 cameras arranged around a human participant. It acquires 3D coordinates of each electrode and automatically labels them. Each electrode has a small reflector on top of it thus allowing its detection by the cameras. We tested the accuracy of the presented method by acquiring the electrodes positions on a rigid sphere model and comparing these with measurements from computer tomography (CT). The average Euclidean distance between the sphere model CT measurements and the presented method was 1.23 mm with an average standard deviation of 0.51 mm. We also tested the method with a human participant. The measurement was quickly performed and all positions were captured. These results tell that, with this method, it is possible to acquire electrode positions with minimal error and little time effort for the study participants and investigators.

Abstract-Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes.Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data.We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.

To prevent the participants of parabolic flights from nausea they are optionally treated with subcutaneously injected antiemetic scopolamine. The range of side effects of this substance include reductions of the α-2 frequency band. Previous studies, however, have given no information as to which generator centers are responsible for this effect. The objective of this study therefore, is to identify the cortex area that may be responsible for this effect. Six participants were subcutaneously administered 0.7 mg of scopolamine. EEG was recorded for 10 minutes before to 20 minutes after injection. Data preprocessing followed including filtering and artifact minimization. A statistical analysis was performed with sLORETA/eLORETA software for each subject over a time window from 3 minutes before to 17-20 minutes after scopolamine injection. Results show, that in the BA7, the precuneus, on both hemispheres suffered a α-2 activity decrease in absolute power. The identified brain cortex center is an important hub with high connectivity and centrality values within the neural network. It contributes to the control of movement and to space orientation. Therefore, an activity alteration in this area can possibly explain the antiemetic effect of scopolamine and open a window to understand the origin of motion sickness.

No abstract