Transfer Learning Based on Optimal Transport for Motor Imagery Brain-Computer Interfaces

Peterson, Victoria; Nieto, Nicolás; Wyser, Dominik; Gassert, Roger; Lambercy, Olivier; Milone, Diego H.; Spies, Rubén D.

doi:10.1109/tbme.2021.3105912

Cited by 22 publications

(15 citation statements)

References 44 publications

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“…Furthermore, classification was performed only sessionwise and based on the entire trial of 16 s. The latter is practicable for pseudo-online classification in the frame of this work, but for a real-time BCI, the decision window should be reduced to a few seconds after the task onset. 88 As an ultimate goal, transfer learning from one session to another, 89 , 90 as well as asynchronous BCI settings (i.e., the task onset is not known), 91 should be addressed to make the step toward in-home applications.…”

Section: Discussionmentioning

confidence: 99%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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. Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( and correlation ). SCR increased the reproducibility from 0.64 to 0.81 ( ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

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Section: Discussionmentioning

confidence: 99%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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“…An alternative class of methods aim at extracting spatial features as manifested in multichannel EEG signals [11][12][13][14][15]. Adaptive classifiers, matrix and tensor classifiers, transfer learning, and deep learning are among other methods that have more recently been proposed [7,16,17].…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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Objective: This paper presents a graph signal processing (GSP)-based approach for decoding two-class motor imagery EEG data via deriving task-specific discriminative features. Methods: First, a graph learning (GL) method is used to learn subject-specific graphs from EEG signals. Second, by diagonalizing the normalized Laplacian matrix of each subject graph, an orthonormal basis is obtained using which the graph Fourier transform (GFT) of the EEG signals is computed. Third, the GFT coefficients are mapped into a discriminative subspace for differentiating two class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT). Finally, an SVM classifier is trained and tested on the variance of the resulting features to differentiate motor imagery classes. Results: The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to i) using features extracted on a graph constructed by Pearson correlation coefficients and ii) three state-of-the-art alternative methods. Conclusion: Experimental results indicate the superiority of the proposed method over alternative methods, reflecting the added benefit of integrating elements from GL, GSP and FKT. Significance: The proposed method and results underpin the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate motor imagery classes.

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“…Wang et al [ 22 ] reviewed the recent advances in domain adaptation and domain generalization, and analyzed the generalization problem in depth which improved the development of machine learning. Recently, an increasing number of researchers who engage in BCI, have focused on using domain adaptation to make the most of the available data from source subjects [ 23 – 28 ]. Chai et al [ 23 ] proposed a novel subspace alignment auto-encoder to reduce the difference in data distribution among subjects or sessions, which combined auto-encoder and subspace alignment in a unified framework by using nonlinear transformations and maximum mean discrepancy (MMD).…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [ 27 ] proposed a cross-device transfer learning framework based on alignment and pooling for EEG headset domain adaptation, which is accomplished by aligning the spatial pattern and covariance of the source and target domains to realize effective transfer. Peterson et al [ 28 ] proposed backward optimal transport for domain adaptation to boost the performance of an already trained classifier by transforming target samples.…”

Section: Introductionmentioning

confidence: 99%

A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification

2022

Appl Intell

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Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t- test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.

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Transfer Learning Based on Optimal Transport for Motor Imagery Brain-Computer Interfaces

Cited by 22 publications

References 44 publications

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification

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