Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

Abstract

The most popular paradigm in BCIs is the steady-state visually evoked potential (SSVEP) due to their advantages, such as the high information transfer rate (ITR), the time spent on users in the training phase, and the capacity to discriminate each stimulus. One of the most influential factors in the ITR evaluation is the feature extraction methods since these can increase the accuracy. Here, we compare nine methods for the extraction of features from SSVEP signals to identify those with better performance, according to the time window (TW), its technology (equipment and number of nodes), and the value of ITR. The study identifies two groups: the first one is characterized by presenting variations of correlated component analysis (CCA), which is highly used to increase the ITR due to its efficiency in classification and its capacity of response to reduction (TW), such as MsetCCA, IT-CCA, FBCCA; the second one are the representation special based methods that consider the non-linear nature of the electroencephalogram (EEG) signal such as TRCA, CORRCA, EMD, and VMD. The results show a considerable difference between these groups. The maximum ITR value for FBCCA was 117.75 [bits/min] in a TW of 1.25s, while the VMD method achieved 3120 [bits/min] in a TW of 1s, respectively. The comparison covers signals between 0.55 and 8 seconds, taking into account visual strain, the experimental environment, and other artifacts.

Coordination Management and Competition Analysis of International Affairs Department Based on MS-VAR Model

“…As exchange rate fluctuations, short-term international capital flows and RMB internationalization all show certain nonlinear characteristics, exchange rates have two states of appreciation and depreciation, capital inflows and outflows, and RMB internationalization has two states of acceleration and deceleration, so using the nonlinear MS-VAR model can better solve such problems. This model allows the regression parameters to vary with time depending on an unobservable regional variable [12]. The formula (3) of MS-VAR (p) model with p-order lag is as follows:…”

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

“…The study also includes other linear approaches such as task-related component analysis (TRCA), correlated component analysis (CORCA), and least absolute shrinkage and selection operator (LASSO). However, as we mentioned before, the EEG signals are nonlinear; so, approaches such as empirical and variational mode decomposition (i.e., EMD and VMD as nonlinear) have had outstanding performances, as mentioned in Labecki et al [28], De la Cruz et al [29].…”