Real-time motion artifact detection and removal for ambulatory BCI

Abstract

Although human cognition often occurs while moving, most studies of the dynamics of the human brain examine subjects while static and seated in a highly controlled laboratory. EEG signals have been considered to be too noisy to record brain dynamics during human locomotion. Here, we present a real-time ambulatory brain computer interface which allows us to detect gait phases and remove motion-related artifacts from EEG signals during walking in real-world environments. We first construct stride-based artifact templates employing a gyroscope to measure the angular velocity of the human body. Then, we apply an adaptive Kalman filter to estimate the mapping between the stride-based artifact template and EEG space, subtracting the motion-related noise from the raw EEG signal. This study demonstrates the robustness of our system to remove gait-related movement artifacts during human locomotion. Experiments in real-world environments show the potential practicality of reallife applications of low-cost wearable and wireless BCI systems for users actively working in and interacting with their environments.

Mobile steady-state evoked potential recording: Dissociable neural effects of real-world navigation and visual stimulation

“…A further problem with ICA is the potential to introduce additional sources of variance as different algorithms can give different results (Pontifex, Gwizdala, Parks, Billinger, & Brunner, 2017). Some researchers have used gait based movement artefact template subtraction to recover steady state visual evoked potentials (SSVEPs) and the P300 component of ERPs during movement (Kim & Jo, 2015). More recently, attempts to remove motion artefacts from mobile EEG using the electrocardiogram (ECG) signal as a reference signal have been able to reduce, but not completely remove, the movement-related artefacts (Butkeviciute et al, 2019).…”

Quality Assessment of Single-Channel EEG for Wearable Devices

“…Appropriate filtering techniques can be applied to physiological recordings (e.g., electrocardiogram or ocular movements), to detect or reduce some artefacts in real time. For instance, motion artefacts can be detected with a gyroscope and subtracted from the raw EEG signal with an appropriate adaptive filter [18]. In the same idea, an approach based on a FIR filter [19,20] can distinguish, on a single EEG channel, ocular artefacts which are detected as irregular spikes.…”