Paper Info
Title
Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels.
Abstract
Local field potentials (LFPs) have been proposed as a neural decoding signal to compensate for spike signal deterioration in invasive brain-machine interface applications. However, the presence of redundancy among LFP signals at different frequency bands across multiple channels may affect the decoding performance. In order to remove redundant LFP channels, we proposed a novel Fisher-distance ratio-based method to actively batch select discriminative channels to maximize the separation between classes. Experimental evaluation was conducted on 5 non-consecutive days of data from a non-human primate. For data from each day, the first experimental session was used to generate the training model, which was then used to perform 4-class decoding of signals from other sessions. Decoding achieved an average accuracy of 79.55%, 79.02% and 79.40% using selected LFP channels for beta, low gamma and high gamma frequency bands, respectively. Compared with decoding using full LFP channels, decoding using selected LFP channels in high gamma band resulted in an increase of 8.67% in accuracy, even if this accuracy was still 7.26% lower than that of spike-based decoding. These results demonstrate the effectiveness of the proposed method in selecting discriminative LFP channels for neural decoding.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512628
EMBC
Field
DocType
Volume
Computer vision,Data modeling,Noise measurement,Pattern recognition,Computer science,Communication channel,Redundancy (engineering),Neural decoding,Local field potential,Artificial intelligence,Decoding methods,Discriminative model
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Huijuan Yang1234.93
Kai Keng Ang280464.19
Camilo Libedinsky323.43
Rosa Q. So4287.42