common spatial pattern (csp) is a method commonly used to enhance the effects of event‑related desynchronization and event‑related synchronization present in multichannel electroencephalogram‑based brain‑computer interface (bci) systems. in the present study, a novel csp sub‑band feature selection has been proposed based on the discriminative information of the features. besides, a distinction ...

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...

In this study, a novel regularized common spatial pattern method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery based brain computer interfaces. Common Spatial Patterns (CSP) method is an effective spatial filter for discriminating different motor imagery signals acquired using large number of EEG electrodes. Unfortunately, CSP method is...

Recently, a large set of electroencephalography (EEG) data is being generated by several high-quality labs worldwide and is free to be used by all researchers in the world. On the other hand, many neuroscience researchers need these data to study different neural disorders for better diagnosis and evaluating the treatment. However, some format adaptation and pre-processing are necessary before ...

Common spatial pattern (CSP) method is highly successful in calculating spatial filters for motor imagery-based brain-computer interfaces (BCIs). However, conventional CSP algorithm is based on a single wide frequency band with a poor frequency selectivity which will lead to poor recognition accuracy. To solve this problem, a novel Partitioned CSP (PCSP) algorithm is proposed to find the most r...

We propose a simultaneous spatio-temporal filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem. The algorithm is a generalization of the Common Spatial Pattern (CSP) algorithm, which incorporates non-homogeneous weighting of the cross-spectrum matrices. We alternately update the spectral weighting coefficients and the spatial projection. The cro...

We propose a simultaneous spatiotemporal filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem. The algorithm is a generalization of the Common Spatial Pattern (CSP) algorithm, which incorporates non-homogeneous weighting of the crossspectrum matrices. The spectral weighting coefficients and the spatial filter are alternately updated. The validat...

Common spatial pattern (CSP) is becoming a standard way to combine linearly multi-channel EEG data in order to increase discrimination between two motor imagery tasks. We demonstrate in this article that the use of robust estimates allows improving the quality of CSP decomposition and CSP-based BCI. Furthermore, a scheme for electrode subset selection is proposed. It is shown that CSP with such...

One of most important tasks or key steps in the designing of an EEG-based BCI system is the optimization of spatio-temporal filters for each subject due to the poor spatial resolution of the EEG recordings, as well as the topographical arrangement and frequency specificity of brain activities. A highly popular technique for the optimization of spatial filters is Common Spatial Pattern (CSP). To...

The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually ...