Statistical Learning for BCIs

Flamary, Rémi; Rakotomamonjy, Alain; Sebag, Michèle

doi:10.1002/9781119144977.ch9

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…BCI aims at providing a direct communication pathway between the human brain and control assistive applications, such as stroke rehabilitation [2], robot and wheelchair control [3], [4], gaming and enhancing user experience [5], [6] and Military [7]. BCI technologies follows the principle that the intent of any action and its subsequent planning originates from the brain, which can be extracted, decoded and analyzed using advanced signal processing, machine learning and statistical algorithms [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Raza

Chowdhury

Bhattacharyya

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Research and development of new machine learning techniques to augment the performance of Brain-computer Interfaces (BCI) have always been an open area of interest among researchers. The need to develop robust and generalised classifiers has been one of the vital requirements in BCI for realworld application. EEGNet is a compact CNN model that had been reported to be generalised for different BCI paradigms. In this paper, we have aimed at further improving the EEGNet architecture by employing Neural Structured Learning (NSL) that taps into the relational information within the data to regularise the training of the neural network. This would allow the EEGNet to make better predictions while maintaining the structural similarity of the input. In addition to better performance, the combination of EEGNet and NSL is more robust, works well with smaller training samples and requires on separate feature engineering, thus saving the computational cost. The proposed approach had been tested on two standard motor imagery datasets: the first being a two-class motor imagery dataset from Graz University and the second is the 4-class Dataset 2a from BCI competition 2008. The accuracy has shown that our combined EEGNet an NSL approach is superior to the sole EEGNet model.

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Section: Introductionmentioning

confidence: 99%

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Raza

Chowdhury

Bhattacharyya

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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show abstract

“…Although neural networks have been used in astronomy since the early nineties (Odewahn et al 1992;Bertin & Arnouts 1996;Tagliaferri et al 2003), the use of deep learning has started to spread only in the last couple of years. Convolutional neural networks (CNN, LeCun et al 1989;Krizhevsky et al 2012) are becoming more and more common for image-related tasks, such as galaxy morphology prediction (Dieleman et al 2015), astronomical image reconstruction (Flamary 2016), photometric redshift prediction (Hoyle 2016), and star-galaxy classification (Kim & Brunner 2017). Other deep neural network architectures, such as autoencoders and generative adversarial networks, have been used for feature-learning in spectral energy distributions of galaxies (Frontera-Pons et al 2017) and for image reconstruction as an alternative to conventional deconvolution techniques (Schawinski et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Supervised detection of exoplanets in high-contrast imaging sequences

González

Absil

Droogenbroeck

2018

A&A

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Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model PSF and subtracting the residual starlight and speckle noise. Aims. In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods. We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA).Results. This study shows the improved sensitivity vs specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from ∼2 to ∼10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false positive level. Conclusions. The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

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Statistical Learning for BCIs

Cited by 2 publications

References 31 publications

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Supervised detection of exoplanets in high-contrast imaging sequences

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