Dimensionality Transcending: A Method for Merging BCI Datasets With Different Dimensionalities

Objective: We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2021-02, Vol.68 (2), p.673-684
Main Authors: Rodrigues, Pedro L. C., Congedo, Marco, Jutten, Christian
Format: Article
Language:English
Subjects:
Bioengineering
Brain-computer interfaces
Data points
Datasets
EEG
Electrodes
Electroencephalography
Engineering Sciences
Geometry
Heterogeneous domain adaptation
Human-computer interface
Interfaces
Isometric
Learning
Life Sciences
Manifolds
Matching
Mathematical analysis
Matrix methods
Proposals
Riemannian geometry
Signal and Image processing
Statistical distributions
Statistics
Symmetric matrices
Symmetric positive definite matrices
Transfer learning
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Summary:Objective: We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (BCI). Method: Our proposal uses a two-step procedure that transforms the data points so that they become matched in terms of dimensionality and statistical distribution. In the dimensionality matching step, we use isometric transformations to map each dataset into a common space without changing their geometric structures. The statistical matching is done using a domain adaptation technique adapted for the intrinsic geometry of the space where the datasets are defined. Results: We illustrate our proposal on time series obtained from BCI systems with different experimental setups (e.g., different number of electrodes, different placement of electrodes). The results show that the proposed method can be used to transfer discriminative information between BCI recordings that, in principle, would be incompatible. Conclusion and significance: Such findings pave the way to a new generation of BCI systems capable of reusing information and learning from several sources of data despite differences in their electrodes positioning.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2020.3010854