Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs

Abstract

Due to the relative noise and artifact insensitivity, steady-state visual evoked potential (SSVEP) has been used increasingly in the study of a brain-computer interface (BCI). However, SSVEP is still influenced by the same frequency component in the spontaneous EEG, and it is meaningful to find a parameter that can avoid or decrease this influence to improve the transfer rate and the accuracy of the SSVEP-based BCI. In this work, with wavelet analysis, a new parameter named stability coefficient (SC) was defined to measure the stability of a frequency, and then the electrode with the highest stability was selected as the signal electrode for further analysis. After that, the SC method and the traditional power spectrum (PS) method were used comparatively to recognize the stimulus frequency from an analogous BCI data constructed from a real SSVEP data, and the results showed that the SC method is better for a short time window data.

Amplitude modulation of steady-state visual evoked potentials by event-related potentials in a working memory task

“…This fact suggests that, except for some determined events such as a cognitive event, there is something else contributing to the SSVEP based on the experimental results, which could include a certain type of potential mental activities or physiological noise. In other words, the SSVEP amplitude includes two parts: one induced by the background noise and the other induced by the repetitive stimulus, with the latter bigger than the former [20]. So, even if the SSVEP has been used to study the long-time cognitive task process in many studies [2,4,5], we still strongly suggest that the explanation about the variance of SSVEP amplitude and phase should be made with more care.…”

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

“…Several classification methods based on frequency features had been proposed for SSVEP-based BCIs (Palaniappan et al, 2002;Lotte et al, 2007;Middendorf et al, 2000;Gao et al, 2003;Lalor et al, 2005;Mukesh et al, 2006;Muller-Putz and Pfurtscheller, 2008;Liavas et al, 1998;Müller-Putz et al, 2005Wu and Yao, 2008). The traditional frequency domain analysis for SSVEP detection was power spectral density-based analysis (PSDA) (Middendorf et al, 2000;Gao et al, 2003;Lalor et al, 2005;Mukesh et al, 2006;Liavas et al, 1998).…”

“…Moreover, an assisted closed loop (ACL) algorithm was present for optimizing spectrum-based SSVEP recognition (Fernandez-Vargas et al, 2013). Wu and Yao (2008) proposed the stability coefficient (SC) model to improve the performance of SSVEP-based BCIs within a short time window of EEG signals.…”

Fast and unique Tucker decompositions via multiway blind source separation

“…How to extract and recognize SSVEP components accurately is one of the crucial issues for SSVEP BCI. Although SSVEP is evoked by a repetitive stimulus with relatively stationary intensity, the spontaneous EEG signal or noise with the same frequency as the stimulus and its harmonics, but having time-varying intensity, may contaminate the SSVEP measured from the scalp and make it an unstable signal [59]. Effective extraction of the true SSVEP components from the EEG signals will help in enhancing the recognition accuracy of stimulus frequency components, thereby improving the performance of SSVEP BCI.…”