The use of an ensemble of local Common Spatial Patterns (CSP) patches (CSPP) is proposed, which can be considered as a compromise between Laplacians and CSP: CSPP reaches a robust performance with less training data than CSP, while being superior to Laplacian filtering. This property is shown to be particularly useful for the co-adaptive calibration design and is demonstrated in combination wit...

EEG single-trial analysis requires methods that are robust against noise and disturbance. In this contribution, based on the framework of robust statistics, we propose a simple modification of common spatial patterns (CSP) by robustifying covariance estimators against outlying trials caused for example by artifacts. We tested the proposed robust filters with EEG recordings from 80 subjects and ...

Appropriate choice of number of electrodes and their positions are essential in Brain-Computer Interface applications since using less electrodes collects insufficient information for classification purposes whereas using more collects redundant information that could degrade BCI performance. This paper proposes a novel method of optimizing EEG channel selection by using the regularized Common ...

This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysi...

The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain–computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically...

Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users’ movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the...

BACKGROUND Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accu...

Laplacian filters are widely used in neuroscience. In the context of brain–computer interfacing, they might be preferred to data-driven approaches such as common spatial patterns (CSP) in a variety of scenarios such as, e.g., when no or few user data are available or a calibration session with a multi-channel recording is not possible, which is the case in various applications. In this paper we...

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectra...

Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their app...