Literature DB >> 12899269

A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.

Paul Sajda1, Adam Gerson, Klaus-Robert Müller, Benjamin Blankertz, Lucas Parra.   

Abstract

We present three datasets that were used to conduct an open competition for evaluating the performance of various machine-learning algorithms used in brain-computer interfaces. The datasets were collected for tasks that included: 1) detecting explicit left/right (L/R) button press; 2) predicting imagined L/R button press; and 3) vertical cursor control. A total of ten entries were submitted to the competition, with winning results reported for two of the three datasets.

Mesh:

Year:  2003        PMID: 12899269     DOI: 10.1109/TNSRE.2003.814453

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  11 in total

1.  A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.

Authors:  Ga-Young Choi; Chang-Hee Han; Young-Jin Jung; Han-Jeong Hwang
Journal:  Gigascience       Date:  2019-11-01       Impact factor: 6.524

2.  A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications.

Authors:  Lei Qin; Bin He
Journal:  J Neural Eng       Date:  2005-08-15       Impact factor: 5.379

3.  A comparison of univariate, vector, bilinear autoregressive, and band power features for brain-computer interfaces.

Authors:  Clemens Brunner; Martin Billinger; Carmen Vidaurre; Christa Neuper
Journal:  Med Biol Eng Comput       Date:  2011-09-25       Impact factor: 2.602

4.  Considerate motion imagination classification method using deep learning.

Authors:  Zhaokun Yan; Xiangquan Yang; Yu Jin
Journal:  PLoS One       Date:  2022-10-20       Impact factor: 3.752

5.  Review of the BCI Competition IV.

Authors:  Michael Tangermann; Klaus-Robert Müller; Ad Aertsen; Niels Birbaumer; Christoph Braun; Clemens Brunner; Robert Leeb; Carsten Mehring; Kai J Miller; Gernot R Müller-Putz; Guido Nolte; Gert Pfurtscheller; Hubert Preissl; Gerwin Schalk; Alois Schlögl; Carmen Vidaurre; Stephan Waldert; Benjamin Blankertz
Journal:  Front Neurosci       Date:  2012-07-13       Impact factor: 4.677

6.  A novel channel selection method for optimal classification in different motor imagery BCI paradigms.

Authors:  Haijun Shan; Haojie Xu; Shanan Zhu; Bin He
Journal:  Biomed Eng Online       Date:  2015-10-21       Impact factor: 2.819

7.  Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.

Authors:  Jaeyoung Shin; Alexander von Lühmann; Do-Won Kim; Jan Mehnert; Han-Jeong Hwang; Klaus-Robert Müller
Journal:  Sci Data       Date:  2018-02-13       Impact factor: 6.444

8.  Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Authors:  Domen Novak; Roland Sigrist; Nicolas J Gerig; Dario Wyss; René Bauer; Ulrich Götz; Robert Riener
Journal:  Front Neurosci       Date:  2018-01-11       Impact factor: 4.677

9.  Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals.

Authors:  Yi Guo; Sinan Gok; Mesut Sahin
Journal:  Front Neurosci       Date:  2018-10-17       Impact factor: 4.677

10.  Motor Imagery Under Distraction- An Open Access BCI Dataset.

Authors:  Stephanie Brandl; Benjamin Blankertz
Journal:  Front Neurosci       Date:  2020-10-19       Impact factor: 4.677

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