Paper Info
Title
Considering Neural Connectivity in Point Process Decoder for Brain-Machine Interface
Abstract
Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9630383
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
DocType
Volume
ISSN
Conference
2021
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Shuhang Chen113.39
Xi Liu200.68
Yiwen Wang312.71