Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset

Abstract

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.

Real-time motion artifact detection and removal for ambulatory BCI

“…At first, significant drops in accuracy were happened between sitting in a laboratory and walking around a building (averaged 14.4% and 14.8% increased for SSVEP and P300 respectively). This results have similar tendency with previous work [5,6]. On the other hand, all results obtained from the proposed system achieved statistically significant improvements (p < 0.05).…”

“…To measure brain dynamics during locomotion, additional information is required to combat the reduced level of measurement and therefore control. Recently, locomotionbased mobile BCI technologies have shown its efficacy during walking and running [4,5,6]. The main idea of these technologies is based on electrocortical activity that is coupled to the gait cycle phase [7].…”

“…The main idea of these technologies is based on electrocortical activity that is coupled to the gait cycle phase [7]. However, most of these studies have been limited to highly-controlled laboratory environments such as walking on a treadmill [5,6]. Unfortunately, applying these technologies in real-world environments cannot be carried out easily because the speed and length of a human's stride are vary in real-world environments.…”

Dynamic motion artifact removal using inertial sensors for mobile BCI

“…In particular, motion artifacts due to body movements share the same frequency spectra with EEG (up to 50Hz) and have an amplitude that is an order of magnitude larger than the underlying brain-related EEG signals [2]. Although some studies have suggested various methods to obtain high quality of EEG signals against motion artifacts, most of these studies have been limited to highly-controlled laboratory environments such as walking on a treadmill [3]. In this paper, we describe a method for removing motion artifacts occurred by body movements using inertial sensors such as an accelerometer and a gyroscope.…”

A motion-artifact tracking and compensation technique for dry-contact EEG monitoring system

“…Therefore, the MA-induced ETI variations are usually characterized as impedance-resistance and capacitance-variations and a time-varying offset voltage based on the lumped circuit model. Most of the prior works in this area are focusing on rejecting the offset or baseline wander using a high-pass filter (HPF) [2][3][4]. A few recent works also reported ETI impedance-variation detection and tracking techniques [5][6].…”