Paper Info
Title
Non-Linear Online Low-Frequency Eeg Decoding Of Arm Movements During A Pursuit Tracking Task
Abstract
Decoding upper-limb movements in invasive recordings has become a reality, but neural tuning in non-invasive low-frequency recordings is still under discussion. Recent studies managed to decode movement positions and velocities using linear decoders, even developing an online system. The decoded signals, however, exhibited smaller amplitudes than actual movements, affecting feedback and user experience. Recently, we showed that a non-linear offline decoder can combine directional (e.g., velocity) and non-directional (e.g., speed) information. In this study, it is assessed if the nonlinear decoder can be used online to provide real-time feedback. Five healthy subjects were asked to track a moving target by controlling a robotic arm. Initially, the robot was controlled by their right hand; then, the control was gradually switched until it was entirely controlled by the electroencephalogram (EEG). Correlations between actual and decoded movements were generally above chance level. Results suggest that information about speed was also encoded in the EEG, demonstrating that the proposed non-linear decoder is suitable for decoding real-time arm movements.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175723
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Víctor Martínez-Cagigal133.11
Reinmar J Kobler200.34
Valeria Mondini300.34
Roberto Hornero460367.74
Gernot Müller-Putz547552.95