LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.
Behavior classification
Convolutional neural networks
Deep brain stimulation
Local field potential
Time-frequency analysis
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 04 2020
01 04 2020
Historique:
received:
29
07
2019
revised:
21
01
2020
accepted:
31
01
2020
pubmed:
7
2
2020
medline:
22
6
2021
entrez:
7
2
2020
Statut:
ppublish
Résumé
Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is often used for patients with Parkinson's disease (PD) when medication cannot effectively tackle patients' motor symptoms. A DBS system capable of adaptively adjusting its parameters based on patients' activities may optimize therapy while reducing the stimulation side effects and improving the battery life. STN-LFP reveals motor and language behavior, making it a reliable source for behavior classification. This paper presents LFP-Net, an automated machine learning framework based on deep convolutional neural networks (CNN) for classification of human behavior using the time-frequency representation of STN-LFPs within the beta frequency range. CNNs learn different features based on the beta power patterns associated with different behaviors. The features extracted by the CNNs are passed through fully connected layers and then to the softmax layer for classification. Our experiments on ten PD patients performing three behavioral tasks including "button press", "target reaching", and "speech" show that the proposed approach obtains an average classification accuracy of ∼88 %. Comparison with existing methods: The proposed method outperforms other state-of-the-art classification methods based on STN-LFP signals. Compared to well-known deep neural networks such as AlexNet, our approach gives a higher accuracy using significantly fewer parameters. CNNs show a high performance in decoding the brain neural response, which is crucial in designing the automatic brain-computer interfaces and closed-loop systems.
Sections du résumé
BACKGROUND
Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is often used for patients with Parkinson's disease (PD) when medication cannot effectively tackle patients' motor symptoms. A DBS system capable of adaptively adjusting its parameters based on patients' activities may optimize therapy while reducing the stimulation side effects and improving the battery life.
METHOD
STN-LFP reveals motor and language behavior, making it a reliable source for behavior classification. This paper presents LFP-Net, an automated machine learning framework based on deep convolutional neural networks (CNN) for classification of human behavior using the time-frequency representation of STN-LFPs within the beta frequency range. CNNs learn different features based on the beta power patterns associated with different behaviors. The features extracted by the CNNs are passed through fully connected layers and then to the softmax layer for classification.
RESULTS
Our experiments on ten PD patients performing three behavioral tasks including "button press", "target reaching", and "speech" show that the proposed approach obtains an average classification accuracy of ∼88 %. Comparison with existing methods: The proposed method outperforms other state-of-the-art classification methods based on STN-LFP signals. Compared to well-known deep neural networks such as AlexNet, our approach gives a higher accuracy using significantly fewer parameters.
CONCLUSIONS
CNNs show a high performance in decoding the brain neural response, which is crucial in designing the automatic brain-computer interfaces and closed-loop systems.
Identifiants
pubmed: 32027889
pii: S0165-0270(20)30043-1
doi: 10.1016/j.jneumeth.2020.108621
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
108621Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None.