title: Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.
abstract: 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.
authors.forename: Yuan-Pin
authors.initials: YP
authors.lastname: Lin
authors.forename: Yijun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, USA. jung@sccn.ucsd.edu.
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
mesh_terms: D000328:Adult; D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D009862:Online Systems; D012984:Software; D014584:User-Computer Interface; D016138:Walking / Q000502:physiology*; D055815:Young Adult
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