Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spa...

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). ...

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 ...

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature ext...

OBJECTIVE Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At th...

With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or sing...

Thispaper presents a novel method for electroencephalography (EEG) based motor imagery classification for brain computer interface (BCI) implementation using the potential features extracted bandspecific common spatial pattern (CSP). The recorded EEG signal is bandpass-filtered into multiple subbands to capture the related rhythmic components of brain signals. The CSP features are then extracte...

We propose a novel framework for the classification of single trial ElectroEncephaloGraphy (EEG), based on regularized logistic regression. Framed in this robust statistical framework no prior feature extraction or outlier removal is required. We present two variations of parameterizing the regression function: (a) with a full rank symmetric matrix coefficient and (b) as a difference of two ran...

Robustness for BCI is usually obtained at the classifier level Non-stationarity is inherent to brain signals We propose to leverage this at the feature extraction level

Curved-surface projection (CSP) is a new technique for visualizing functional MR imaging data. This technique helps in identifying anatomic structures by demonstrating the whole gyral and sulcal pattern of the brain at once. Compared with other techniques, CSP preserves the spatial relation of eloquent areas to lesions. Especially in neurosurgical patients with space-occupying lesions, CSP help...