International Journal on Advanced Science, Engineering and Information Technology, Vol. 11 (2021) No. 2, pages: 761-768, DOI:10.18517/ijaseit.11.2.14309

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

Danni De la Cruz, Wilfredo Alfonso, Eduardo Caicedo

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.

Keywords:

Steady-state visually evoked potential; brain-computer interfaces; information transfer rate; canonical correlation analysis; empirical mode decomposition.

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