In neurology and neuroscience research, Steady-State Visually Evoked Potential (SSVEP) are brain signals which occur in response to visual stimulation. The paper Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed – written by Abdullah Talha Sözer and Can Bülent Fidan – aims to introduce novel detection features for the SSVEP based brain computer interfaces. Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables direct communication between the brain and the object to be controlled.
In this study, these new detection features are called coefficient of variation (CV) and variation speed (VS). They are investigated by using the stability of the SSVEP signal. The proposed features are tested on SSVEP datasets.
The stability of the SSVEP signal was examined by using wavelet analysis (wavelet – wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero). After the results are analyzed, it is seen that CV and VS features provide detection results similar to PSD(power spectral density), which is a familiar feature.
Despite better detection accuracies being obtained by PSD in most of the EEG data, the
proposed features have better results detection accuracies in some EEG data. This suggests that by using familiar features and suggested features together, a method that gives higher SSVEP detection accuracy can be developed.
Read more here!