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Open AccessJournal ArticleDOI

Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis.

TLDR
A newly developed standard for presenting results acquired during MIBCI experiments is proposed, designed to facilitate communication and comparison of essential information regarding the effects observed, based on the findings of descriptive analysis and meta-analysis.

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Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

TL;DR: In this paper, the authors present the most relevant aspects of the BCI and all the milestones that have been made over nearly 50-year history of this research domain and highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many.
Journal ArticleDOI

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.

TL;DR: Three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes.
Journal ArticleDOI

Brain-Computer Interface: Advancement and Challenges.

TL;DR: In this paper, a comprehensive overview of the brain-computer interface (BCI) domain is presented, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers.
Posted Content

Pairwise Comparisons Simplified

TL;DR: An algorithm of reconstructing of the PC matrix from its set of generators is presented, which decreases the number of pairwise comparisons from n ?
Journal ArticleDOI

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

TL;DR: Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology, which shows that classical machine learning approaches are still effective for binary classifications.
References
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Journal ArticleDOI

Measuring inconsistency in meta-analyses

TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
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Event-related EEG/MEG synchronization and desynchronization: basic principles.

TL;DR: Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously.
Journal ArticleDOI

An introduction to meta-analysis.

TL;DR: This paper reviews the use ofMeta-Analysis as a data pooling technique in a non-technical manner and illustrates the type of information that can be obtained from a Meta-Analysis, that is not conventionally available from individual trials.
Journal ArticleDOI

Removing electroencephalographic artifacts by blind source separation.

TL;DR: The results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods.
Journal ArticleDOI

A review of classification algorithms for EEG-based brain–computer interfaces

TL;DR: This paper compares classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG) in terms of performance and provides guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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