3D Hand Movement Velocity Reconstruction using Power Spectral Density of EEG Signals and Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages4
Publication statusPublished - 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Milan, Italy
Duration: 1 Jan 2015 → …

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period1/01/15 → …

Keywords

  • EEG
  • neural network
  • BCI
  • brain-computer interface
  • motion trajectory
  • hand movement
  • 3d
  • prediction

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