Impact Factor 3.209

The 1st most cited journal in Psychology

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Hum. Neurosci. | doi: 10.3389/fnhum.2017.00560

Decoding of self-paced lower-limb movement intention: A case study on the influence factors

  • 1Beihang University, China
  • 2École Polytechnique Fédérale de Lausanne, Switzerland

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was -0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5 % and 68.2 ± 4.6 % was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here showed the applicability of the proposed method in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.

Keywords: Brain-machine interface (BMI), Electroencephalography (EEG), lower-limb movement, onset detection, Movement-related cortical potentials (MRCPs), sensory motor rhythms (SMRs)

Received: 26 Aug 2017; Accepted: 06 Nov 2017.

Edited by:

Mikhail Lebedev, Duke University, United States

Reviewed by:

Noman Naseer, Air University, Pakistan
Giovanni Pellegrino, IRCCS Fondazione Ospedale San Camillo, Italy  

Copyright: © 2017 Liu, Chen, Chavarriaga, Pei and Millán. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Weihai Chen, Beihang University, Beijing, China,
Prof. José del R Millán, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland,