Optimal spectral tracking--with application to speed dependent neural modulation of tibialis anterior during human treadmill walking

Research output: Contribution to journalArticlepeer-review


  • John-Stuart Brittain
  • Celia Catton
  • Bernard A Conway
  • Jens Bo Nielsen
  • David M Halliday

Colleges, School and Institutes

External organisations

  • University of York


A novel method of optimal spectral tracking is presented which permits the characterisation of trial-varying parameters. Many experimental studies suffer from the limitations of available analysis methodologies, which often impose a condition of stationarity. This severely limits our ability to track slow varying or dynamic responses with any statistical certainty. Presented is a complete framework for the non-stationary analysis of trial-varying data. Theory is introduced and developed in the characterisation of speed dependent neural modulation of the locomotor drive to tibialis anterior (TA) during healthy treadmill locomotion. The approach adopts adaptive filter theory while retaining a spectral focus, thus remaining compatible with much of the current literature. Spectral tracking procedures are evaluated using both surrogate and neurophysiological time-series. Confidence intervals are derived in both empiric and numerical form. Analysis of the pre-synaptic drive to TA under the modulation of treadmill belt speed follows, with results demonstrating clear speed dependent influences on the spectral content of TA, suggesting dynamic neural modulation of the locomotor drive. Findings include speed-modulated components at 7-12Hz (early swing) and 15-20Hz (pre-stance). Speed invariant components were identified at 8-15 and 15-20Hz during early and late swing, in agreement with previous studies. Modification to the method permits a sub-optimal alternative, encouraging the exploration of short epoched data.


Original languageEnglish
Pages (from-to)334-47
Number of pages14
JournalJournal of Neuroscience Methods
Issue number2
Publication statusPublished - 15 Mar 2009


  • Algorithms, Biomechanical Phenomena, Electromyography, Exercise Test, Fourier Analysis, Gait, Humans, Leg, Locomotion, Muscle Contraction, Muscle, Skeletal, Neurophysiology, Signal Processing, Computer-Assisted, Walking, Journal Article, Research Support, Non-U.S. Gov't