Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset

Background: bridging the gap between laboratory brain-computer interface (bci) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. an urgent need is to explore the feasibility of using a low-cost,ease-of-use electroencephalogram (eeg) headset for monitoring individuals' eeg signals in their natural head/body positions and movements. this study aimed to assess the feasibility of using a consumer-level eeg headset to realize an online steady-state visual-evoked potential (ssvep)-based bci during human walking.methods. this study adopted a 14-channel emotiv eeg headset to implement a four-target online ssvep decoding system,and included treadmill walking at the speeds of 0.45,0.89,and 1.34 meters per second (m/s) to initiate the walking locomotion. seventeen participants were instructed to perform the online bci tasks while standing or walking on the treadmill. to maintain a constant viewing distance to the visual targets,participants held the hand-grip of the treadmill during the experiment. along with online bci performance,the concurrent ssvep signals were recorded for offline assessment.results: despite walking-related attenuation of ssveps,the online bci obtained an information transfer rate (itr) over 12 bits/min during slow walking (below 0.89 m/s).conclusions: ssvep-based bci systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. this study considerably promotes the use of a consumer-level eeg headset towards the real-life bci applications. © 2014lin et al.; licensee biomed central ltd.