Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we i...

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 ...

The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to le...

introduction: in most bci articles which aim to separate movement imaginations, µ and β frequency bands have been used. in this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well. methods: in this study we used data set 2a from bci competition iv. in this data set, 9 healthy subjects have perfor...

Classification of multichannel electroencephalogram (EEG) recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). Frequency bands and channels configuration that relate to brain activities associated with BCI tasks are often pre-decided as default in EEG analysis without deliberations. However, a steady configuration usually loses effects due to ...

Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. This paper presents a method for classifying EEG during motor-imagery by the combination of well-known common spatial pattern (CSP) with so-called multivariate empirical mode decomposition (MEMD), which ...

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...