Estimating brain periodic sources activities in steady-state visual evoked potential using local fourier independent component analysis

Tabanfar, Zahra; Ghassemi, Farnaz; Moradi, Mohammad Hassan

doi:10.1016/j.bspc.2021.103162

Cited by 7 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, MFCCA may end up requiring long processing time, which may limit the ability of MFCCA to be applied to problems with high complexity or in real-time systems. In single-frequency SSVEP, the decoding problem has also been approached by treating it as a blind source separation problem, where principal component analysis (PCA) and independent component analysis (ICA) are widely used, and decoding algorithms such as minimum energy combination (MEC) [22] based on PCA and local Fourier-ICA [23] based on ICA have been developed. However, multifrequency SSVEP decoding is a more complex problem to solve as it contains nonlinear interactions of multiple input frequencies in the recorded EEG.…”

Section: Introductionmentioning

confidence: 99%

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

Tan

Grayden

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

Multifrequency steady‐state visual evoked potentials (SSVEPs) have been developed to extend the capability of SSVEP‐based brain‐machine interfaces (BMIs) to complex applications that have large numbers of targets. Even though various multifrequency stimulation methods have been introduced, the decoding algorithms for multifrequency SSVEP are still in early development. The recently developed multifrequency canonical correlation analysis (MFCCA) was shown to be a feasible training‐free option to use in decoding multifrequency SSVEPs. However, the time complexity of MFCCA is shown to be , which will lead to long computation time as grows, where represents the input size in decoding. In this paper, a novel decoding algorithm is proposed with the aim to reduce the time complexity. This algorithm is based on linear Diophantine equation solvers and has a reduced computation cost while remaining training‐free. Our simulation results demonstrated that linear Diophantine equation (LDE) decoder run time is only one fifth of MFCCA run time under respective optimal settings on 5‐s single‐channel data. This reduced computation cost makes it easier to implement multifrequency SSVEP in real‐time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants when using dry electrode scalp electroencephalography (EEG).

show abstract

Section: Introductionmentioning

confidence: 99%