Learning discriminative patterns for self-paced EEG-based motor imagery detection
Open Access
- 1 January 2012
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Neuroscience
- Vol. 6, 7
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.Keywords
This publication has 26 references indexed in Scilit:
- Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer InterfacingIEEE Transactions on Biomedical Engineering, 2006
- The BCI competition III: validating alternative approaches to actual BCI problemsIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
- The Berlin brain-computer interface: EEG-based communication without subject trainingIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
- Continuous EEG classification during motor imagery-simulation of an asynchronous BCIIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2004
- Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigmsIEEE Transactions on Biomedical Engineering, 2004
- Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface projectIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
- Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysisIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
- Implementation of a telemonitoring system for the control of an EEG-based brain-computer interfaceIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
- A brain-controlled switch for asynchronous control applicationsIEEE Transactions on Biomedical Engineering, 2000
- Time-delay neural networks: representation and induction of finite-state machinesIEEE Transactions on Neural Networks, 1997