Effect of walking speed on gait sub phase durations.

Gait phase durations are important spatiotemporal parameters in different contexts such as discrimination between healthy and pathological gait and monitoring of treatment outcomes after interventions. Although gait phases strongly depend on walking speed, the influence of different speeds has rarely been investigated in literature. In this work, we examined the durations of the stance sub phases and the swing phase for 12 different walking speeds ranging from 0.6 to 1.7 m/s in 21 healthy subjects using infrared cinematography and an instrumented treadmill. We separated the stance phase into loading response, mid stance, terminal stance and pre-swing phase and we performed regression modeling of all phase durations with speed to determine general trends. With an increasing speed of 0.1m/s, stance duration decreased while swing duration increased by 0.3%. All distinct stance sub phases changed significantly with speed. These findings suggest the importance of including all distinct gait sub phases in spatiotemporal analyses, especially when different walking speeds are involved.

Methodological aspects of EEG and body dynamics measurements during motion.

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.

Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data.

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.