simBCI—A Framework for Studying BCI Methods by Simulated EEG

Lindgren, Jussi; Merlini, Adrien; Lécuyer, Anatole; Andriulli, Francesco P.

doi:10.1109/tnsre.2018.2873061

Cited by 15 publications

(14 citation statements)

References 46 publications

(56 reference statements)

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“…Simulated EEG data were generated using the simBCI software library (Lindgren et al, 2018 ). Simulation parameters were set according to BCI Competition IV data generation example described in Lindgren et al ( 2018 ) with the following modifications: trial lengths were set to 4,000 ms, sampling frequency set to 250 Hz, and all eye movement/blink effects removed. All simulations were run using the MRI volume-derived leadfield model contained in the “leadfield-mediumRefinement.mat” file available for download with the simBCI software.…”

Section: Methodsmentioning

confidence: 99%

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

Ivanov

Chau²

2023

Front. Comput. Neurosci.

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Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.

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Section: Methodsmentioning

confidence: 99%

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

Ivanov

Chau²

2023

Front. Comput. Neurosci.

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“…Therefore, feature selection step is introduced in the processing pipeline to optimize the estimated features. In literature, various feature selection methods have been proposed for instance Locally Robust Feature Selection (LRFS), 50 CSP based feature selection, 51 Universum Support Vector Machine‐Recursive Feature Elimination (USVM‐RFE), 24 and maximum relevance CCA 17 . In present study, an amalgamation of two feature selection criteria viz.…”

Section: Methodsmentioning

confidence: 99%

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

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Multi-class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi-channel EEG activity still remains a challenging task. In this study, to enhance the multi-class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed.First, multi-channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA-SOGWO based EEG channel selection criterion.After that, the discriminable feature-set is generated from time, frequency, and FAWT based time-frequency domain of EEG dataset. Then, an informative feature-set is constructed from extracted features through CCA-RFE based feature selection criterion. Finally, training and validation of selected feature-set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi-class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

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“…To test the approach on datasets that have a level of complexity comparable to biological data (e.g., EEG and the artifacts that corrupt EEG signals) yet also have a known ground truth, we used the simBCI package (Lindgren et al, 2018) to generate 249electrode EEG data based on a realistic head model. The simulation included ocular artifacts referred to in the paper as eye blinks generated by each eye.…”

Section: Simulated Eeg Datasetsmentioning

confidence: 99%

Application of a Hermite-based measure of non-Gaussianity to normality tests and independent component analysis

Jain

Knight

Victor

2023

Front. Neuroinform.

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In the analysis of neural data, measures of non-Gaussianity are generally applied in two ways: as tests of normality for validating model assumptions and as Independent Component Analysis (ICA) contrast functions for separating non-Gaussian signals. Consequently, there is a wide range of methods for both applications, but they all have trade-offs. We propose a new strategy that, in contrast to previous methods, directly approximates the shape of a distribution via Hermite functions. Applicability as a normality test was evaluated via its sensitivity to non-Gaussianity for three families of distributions that deviate from a Gaussian distribution in different ways (modes, tails, and asymmetry). Applicability as an ICA contrast function was evaluated through its ability to extract non-Gaussian signals in simple multi-dimensional distributions, and to remove artifacts from simulated electroencephalographic datasets. The measure has advantages as a normality test and, for ICA, for heavy-tailed and asymmetric distributions with small sample sizes. For other distributions and large datasets, it performs comparably to existing methods. Compared to standard normality tests, the new method performs better for certain types of distributions. Compared to contrast functions of a standard ICA package, the new method has advantages but its utility for ICA is more limited. This highlights that even though both applications—normality tests and ICA—require a measure of deviation from normality, strategies that are advantageous in one application may not be advantageous in the other. Here, the new method has broad merits as a normality test but only limited advantages for ICA.

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simBCI—A Framework for Studying BCI Methods by Simulated EEG

Cited by 15 publications

References 46 publications

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Application of a Hermite-based measure of non-Gaussianity to normality tests and independent component analysis

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