Riemannian Procrustes Analysis: Transfer Learning for Brain-Computer Interfaces

Objective: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain-computer interfaces (BCI) and poses a challenge for...

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Vydané v:IEEE transactions on biomedical engineering Ročník 66; číslo 8; s. 2390 - 2401
Hlavní autori: Rodrigues, Pedro Luiz Coelho, Jutten, Christian, Congedo, Marco
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Algorithms
Brain-computer interface
Brain-Computer Interfaces
Calibration
Classification
Computer simulation
covariance matrices
Data points
Datasets
EEG
Electroencephalography
Electroencephalography - methods
Engineering Sciences
Geometry
Human-computer interface
Humans
Interfaces
Learning
Manifolds
Matching
Mathematical analysis
Matrix methods
Reuse
Riemannian geometry
Scaling
Shape
Signal and Image processing
Signal Processing, Computer-Assisted
Statistical analysis
Statistical distributions
Statistics
Symmetric matrices
Transfer learning
Riemannian geometry
Brain-Computer Interface
Transfer Learning
EEG
Covariance Matrices
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:Objective: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain-computer interfaces (BCI) and poses a challenge for systems that try to reuse data from previous recordings to avoid a calibration phase for new users or new sessions for the same user. Method: We propose a method based on Procrustes analysis for matching the statistical distributions of two datasets using simple geometrical transformations (translation, scaling, and rotation) over the data points. We use symmetric positive definite matrices (SPD) as statistical features for describing the EEG signals, so the geometrical operations on the data points respect the intrinsic geometry of the SPD manifold. Because of its geometry-aware nature, we call our method the Riemannian Procrustes analysis (RPA). We assess the improvement in transfer learning via RPA by performing classification tasks on simulated data and on eight publicly available BCI datasets covering three experimental paradigms (243 subjects in total). Results: Our results show that the classification accuracy with RPA is superior in comparison to other geometry-aware methods proposed in the literature. We also observe improvements in ensemble classification strategies when the statistics of the datasets are matched via RPA. Conclusion and significance: We present a simple yet powerful method for matching the statistical distributions of two datasets, thus paving the way to BCI systems capable of reusing data from previous sessions and avoid the need of a calibration procedure.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2018.2889705