Feature Extraction In Brain-Computer Interface Research Articles

Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

The analytic common spatial patterns method for EEG-based BCI data

One of the most important stages in a brain–computer interface (BCI) system is that of extracting features that can reliably discriminate data recorded during different user states. A popular technique used for feature extraction in BCIs is the common spatial patterns (CSP) method, which provides a set of spatial filters that optimally discriminate between two classes of data in the least-squares sense. The method also yields a set of spatial patterns that are associated with the most relevant activity for distinguishing between the two classes. The high recognition rates that have been achieved with the method have led to its widespread adoption in the field. Here, a variant of the CSP method that considers EEG data in its complex form is described. By explicitly considering the amplitude and phase information in the data, the analytic CSP (ACSP) technique can provide a more comprehensive picture of the underlying activity, resulting in improved classification accuracies and more informative spatial patterns than the conventional CSP method. In this paper, we elaborate on the theoretical aspects of the ACSP algorithm and demonstrate the advantages of the method through a number of simulations and through tests on EEG data.

Quantification and visualisation of differences between two motor tasks based on energy density maps for brain–computer interface applications

Objective To determine the most discriminative features for a brain–computer interface (BCI) system based on statistically significant differences between two energy density maps calculated from EEG signals during two different motor tasks. Methods EEG was recorded in ten healthy volunteers while performing different cue based, 3 s sustained, real and imaginary right hand movements. Energy density maps were calculated over fixed 240 ms and 2 Hz time–frequency windows (called resels) for each movement and statistically significant resels were determined. After that, normalised energy values of the statistically significant resels were compared between two real as well as between two imaginary movements using a parametric test. Results The largest differences between energy density maps between two motor tasks were noticed on electrode location Cp3 in the higher alpha and the beta bands (i.e., 12–30 Hz), for both real and imaginary movements. The method reduced a total number of discriminative features between two motor tasks to fewer than 2% for the imaginary and fewer than 3% for the real movements on the electrode location Cp3. Conclusions The method can be used for visualisation and feature extraction for BCI and other applications where event related desynchronisation/synchronisation (ERD/ERS) maps should be compared. Significance If a reliable on-line classification of imaginary movements of the same limb would be achieved it could be combined with classification of movements of different parts of the body. That would increase a number of separable classes of a BCI system, thereby providing a larger number of command signals to control the external devises such as computers and robotic devices.