The phrase "ica method" is grammatically correct and can be used in written English.
It refers to a specific technique or approach called Independent Component Analysis. Example: "In her research, Dr. Smith utilized the ica method to analyze the brain activity of participants in the study."
Our results indicate that adequate high-pass filtering may be more important than the choice of the ICA method: all three ICA methods achieved a better ERD peak score when the data had been high-pass-filtered at the cutoff frequency just below the frequency band of interest before decomposition.
The original ICA method often converges to local optima.
The information content assessment (ICA) method is proposed for solution evaluation.
A modified independent component analysis (ICA) method is introduced to separate the internal signals from the measured signals.
In this paper, we introduce a new kernel-based nonlinear ICA method and compare it to standard BSS techniques.
In this paper independent component analysis (ICA) method for amplitude and frequency estimation of highly distorted power system signals is presented.
The brain activated regions were extracted by the SPM and ICA methods respectively.
One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA.
The method designed is able to extract the mixing matrix and some statistical features of the noise and the sources, notably improving the performance of the standard ICA methods when the mixture is contaminated by multiplicative noise.
ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal.
We first extend ICA methods and relax the independence assumption.
