In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly us...

Common spatial pattern (CSP) is widely used for constructing spatial filters to extract features for motor-imagery-based BCI. One main parameter in CSP-based classification is the number of spatial filters used. An automatic method relying on Rayleigh quotient is presented to estimate its optimal value for each subject. Based on an existing dataset, we validate the contribution of the proposed ...

We propose a novel spectral filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem. The algorithm is designed to improve the classification accuracy of Common Spatial Pattern (CSP) based classifiers. The algorithm is based on a simple statistical criterion, and allows the user to incorporate any prior information one has about the spectrum of the ...

The Common Spatial Pattern (CSP) algorithm is a popular method for calculating spatial lters in motor imagery braincomputer interface system. However, the performance of CSP is in uenced by several factors such as the selection of channels, time window length, frequency band and etc.. One of the existing problems is that some above parameters for CSP is tuned manually in most of papers in the l...

Searching for targets in large volume imagery is a challenging problem. A potentially promising solution, rapid image triage (RAIT), is essentially a cortically-coupled computer vision technique based on single-trial detection of event-related potentials (ERP). This paper presents a novel feature selection method, termed common spatio-temporal patterns (CSTP), which is critical for robust singl...

We propose to cluster class-wise covariance matrices in order to identify different groups of covariances contributing to the same condition. Each cluster represents a different brain pattern associated with one class. Further, we present Clustered Common Spatial Patterns, a new algorithm that applies this technique prior to CSP. We show that CCSP can outperform CSP in a binary imagery movement...

the brain – computer interface (bci) provides a communicational channel between human and machine. most of these systems are based on brain activities. brain computer-interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. the success of this methodology depends on the selection of methods to process the brain signals in each pha...

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

Common spatial pattern (CSP) algorithm and principal component analysis (PCA) are two commonly used key techniques for EEG component selection and EEG feature extraction for EEG-based braincomputer interfaces (BCIs). However, both the ordinary CSP and PCA algorithms face a loading problem, i.e., their weights in linear combinations are non-zero. This problem makes a BCI system easy to be over-f...