BCI Competition IV - Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection.

Frontiers in Neuroscience
Haihong ZhangChuanchu Wang

Abstract

Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.

Citations

Oct 15, 2013·Artificial Intelligence in Medicine·Robert LeebJosé D R Millán
May 15, 2013·Nucleic Acids Research·Pralay MitraYang Zhang
Jan 1, 2015·Asian Medicine : Tradition and Modernity·Leslie de Vries
Apr 11, 2015·Water Science and Technology : a Journal of the International Association on Water Pollution Research·Shenbei ZhouMinghao Bai
Oct 24, 2017·Frontiers in Neuroscience·Andres M Alvarez-MezaGerman Castellanos-Dominguez

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Methods Mentioned

BETA
feature extraction

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