Methodological Aspects of Recording and Analyzing Electroencephalographic Signals during Locomotion

Movement artifacts (MA) in electroencephalographic (EEG) signals originate from mechanical forces applied to the scalp electrodes, inducing small electrode movements relative to the scalp which, in turn, cause the recorded voltage to change irrespectively of cortical activity. These mechanical forces, and thus MA, may have various sources that are inherent to daily activities (e.g., ground reaction forces, head movements, etc.). In this talk, experimental measures to minimize MA in EEG and hardware advancements designed for MA reduction will be introduced. We will describe our study, aiming to (i) quantify MA in EEG during walking at different speeds and to (ii) asses our ability to prune it at various intensities (i.e., walking speeds) using state-of-the-art signal processing algorithms. Participants wore a 32-channel EEG cap while walking at various speeds both over-ground and on a treadmill. Data preprocessing included separating the EEG signals into statistically independent additive components using independent component analysis. MA was identified and quantified for each component by computing the spectral energy around a trial-specific stepping frequency. We developed an approach to remove MA contaminated components from the EEG signals. Our analyses suggest that the methodology effectively removes MA, even at high walking speeds.