Faculty Collaboration Database

Idle state classification using spiking activity and local field potentials in a brain computer interface. Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:1572-1575

Date

03/09/2017

Pubmed ID

28268628

DOI

10.1109/EMBC.2016.7591012

Scopus ID

2-s2.0-85009083903 (requires institutional sign-in at Scopus site)   8 Citations

Abstract

Previous studies of intracortical brain-computer interfaces (BCIs) have often focused on or compared the use of spiking activity and local field potentials (LFPs) for decoding kinematic movement parameters. Conversely, using these signals to detect the initial intention to use a neuroprosthetic device or not has remained a relatively understudied problem. In this study, we examined the relative performance of spiking activity and LFP signals in detecting discrete state changes in attention regarding a user's desire to actively control a BCI device. Preliminary offline results suggest that the beta and high gamma frequency bands of LFP activity demonstrated a capacity for discriminating idle/active BCI control states equal to or greater than firing rate activity on the same channel. Population classifier models using either signal modality demonstrated an indistinguishably high degree of accuracy in decoding rest periods from active BCI reach periods as well as other portions of active BCI task trials. These results suggest that either signal modality may be used to reliably detect discrete state changes on a fine time scale for the purpose of gating neural prosthetic movements.

Author List

Williams JJ, Tien RN, Inoue Y, Schwartz AB

Author

Jordan J. Williams MD, PhD Assistant Professor in the Biomedical Engineering department at Marquette University

MESH terms used to index this publication - Major topics in bold

Biomechanical Phenomena
Brain-Computer Interfaces
Electroencephalography
Humans
Motor Cortex
Movement