Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

Abstract

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm

“…These results are advantageous and somewhat superior to systems that integrate the design of the assistive tool, as indicated in references [ 1 , 10 ]. It is important to note that research does exist with accuracy levels of 90% or better, as demonstrated by references [ 15 , 17 , 28 ], and others. However, it is crucial to recognize that the use of complex algorithms directly affects the computational cost.…”

“…The authors of [ 16 ] have developed and implemented a novel approach for enhancing the signal-to-noise ratio in SSVEP by utilizing music. The article [ 17 ] analyzes and compares the utilization of nonlinear canonical correlation analysis (NLCCA) with the CCA and LASSO techniques. The data indicate that NLCCA exhibits enhanced accuracy mainly during the first two seconds of stimulation.…”

DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis

“…Literature Review: Generally speaking, there are two main visual BCI Paradigms, (1) Steady-State Visually Evoked Potential (SSVEP) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], where light flashing (flickering) visual stimulus is used to induce evoked potentials in the EEG signals, and; (2) Steady-State motion Visual Evoked Potentials (SSmVEP) [ 15 , 16 , 17 , 18 , 19 ], where instead of using flickering, some form of graphical motion is used to evoke potentials. The former category (SSVEP) has been the main research theme due to its high achievable Information Transfer Rate (ITR), minimal requirement for user training, and excellent interactive potentials, such as high tolerance to artifacts and robust performance across users.…”