LASSO based stimulus frequency recognition model for SSVEP BCIs

Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces

“…This method has been applied for SSVEP recognizing proposed by Zhang et al (2012). The LASSO model considers the EEG signal response x as the linear output of standard squarewave signals Y elicited by flickering lights at different frequencies:…”

“…In addition, an unsupervised least absolute shrinkage and selection operator (LASSO) model was applied to recognize SSVEP signals to achieve the better effect than that of CCA in a short time window (e.g. 2 s) (Zhang et al, 2012). Recently, a new multivariate synchronization index (MSI) algorithm was proposed for SSVEP recognition .…”

A Novel Combination of Time Phase and EEG Frequency Components for SSVEP-Based BCI

“…In recent years, SSVEP-based BCIs have been increasingly studied and applied in many aspects, such as letter or icon selection, cursor movement and device control, due to the shorter calibration time and higher information transfer rate (ITR) than other types of BCIs [2], [3]. SSVEP evoked by repetition flicker stimulation has the same fundamental frequency as the stimulus and may also include higher harmonics [4].…”

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

“…So far, a large number of methods have been introduced to EEG analysis for various applications [14,15,16,17,18,19,20]. Common spatial pattern (CSP) is a very efficient method and has been mostly applied to MI feature extraction [21,22].…”