Comparative analysis of neural decoding algorithms for brain-machine interfaces

Accurate neural decoding of brain dynamics remains a significant and open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of t...

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Vydáno v:bioRxiv
Hlavní autoři: Shevchenko, Olena, Yeremeieva, Sofiia, Laschowski, Brokoslaw
Médium: Paper
Jazyk:angličtina
Vydáno: Cold Spring Harbor Cold Spring Harbor Laboratory Press 10.12.2024
Cold Spring Harbor Laboratory
Vydání:1.1
Témata:
Algorithms
Classification
Comparative analysis
Computers
Fourier analysis
Fourier transforms
Information processing
Interfaces
Long short-term memory
Neural coding
Neural networks
Neuroscience
Signal processing
ISSN:2692-8205, 2692-8205
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Popis
Shrnutí:Accurate neural decoding of brain dynamics remains a significant and open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies evaluating different combinations of state-of-the-art algorithms for motor neural decoding to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that help inform the design of next-generation neural decoding algorithms for brain-machine interfaces used to interact with and control robots and computers.Competing Interest StatementThe authors have declared no competing interest.
Bibliografie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2024.12.05.627080