Integrated Transfer Learning Based on Group Sparse Bayesian Linear Discriminant Analysis for Error-Related Potentials Detection

2020 ◽  
Author(s):  
Jing Wang ◽  
Tianyou Yu ◽  
Zebin Huang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Transfer Learning ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Bayesian Linear Discriminant Analysis

scholarly journals Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020 ◽  
Author(s):  
Jan Sosulski ◽  
Jan-Philipp Kemmer ◽  
Michael Tangermann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Covariance Matrix ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Feature Representation ◽  
Training Data ◽  
Linear Discriminant ◽  
Related Potentials

AbstractElectroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

scholarly journals Taking brain-computer-interfacing one step further: a portable, wireless system coupled with online linear discriminant analysis for the detection of error-related potentials

2019 ◽  
Vol 299 ◽  
pp. 02004
Author(s):  
Dorina Ancău ◽  
Nicolae-Marius Roman ◽  
Mihai Ancău
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Assistive Technologies ◽  
Neural Signals ◽  
Linear Discriminant ◽  
Essential Condition ◽  
Related Potentials ◽  
One Step ◽  
Brain Computer Interfacing ◽  
Post Hoc

Recent years have witnessed extensive developments of computer science applications in medicine - assistive technologies. Among them, the concept of Brain-Computer-Interfaces, facilitating direct communication between brain and computer, has inspired numerous practical ideas on controlling an external device via neural signals. The perception of an error made by oneself, another human or a machine, triggers an error-related potential, which has already been exploited as a binary correction readout for decisions made by Brain-ComputerInterfaces. Our approach takes advantage of this technique, while taking it one step further regarding portability by using an affordable, robust and wireless headset, the Emotiv EPOC+, to recognize error-related potentials in electroencephalograms of subjects performing various on-site, dynamic tasks. We also introduce a straightforward linear-discriminant analysis classifier that extends the range of detection from offline, post-hoc analysis, to online, within-trial recordings, an essential condition towards blending machine-performed tasks with human-generated thought processes in everyday life.

Sign in / Sign up

Export Citation Format

Share Document