A Novel Fast ICA-FBCCA Algorithm and Convolutional Neural Network for Single-Flicker SSVEP-Based BCIs

Abstract

Brain-computer interface (BCI) systems have been developed to assist individuals with neuromuscular disorders to communicate with their surroundings using their brain signals. One attractive branch of BCI is steady-state visual evoked potential (SSVEP), which has acceptable speed and accuracy and is non-invasive. However, SSVEP-based EEG signals suffer from eye-fatigue problems, resulting in artifacts that affect the accuracy of the system. Thus, researchers are still working to improve SSVEP-based BCI systems. This paper proposes robust machine-learning algorithm for single-flicker SSVEP detection. A novel approach based on fast independent component analysis and filter-bank canonical correlation analysis (fast ICA-FBCCA) is developed to extract features from the single-flicker SSVEP signal. The clean features learned by fast ICA-FBCCA are then applied to a discrete wavelet transform (DWT) technique and fed to a convolutional neural network (CNN) with only one convolutional layer and a smaller number of parameters. The effectiveness of the proposed technique is evaluated using two datasets. The results were evaluated using two datasets. The findings clearly demonstrate that the proposed method outperforms traditional methods, with average target recognition accuracy and standard deviation values of 97 ± 3.1% among 6 subjects for dataset 1 and 82.12 ± 10.7% among 12 subjects for dataset 2. Overall, these findings suggest that the proposed method is a promising approach for improving the accuracy and reliability of the single-flicker SSVEP-based BCI systems. INDEX TERMSbrain-computer interface (BCI); single-flicker steady-state visual evoked potential; fast independent component analysis (fast ICA); filter-bank canonical correlation analysis (FBCCA); convolutional neural network (CNN) I. INTRODUCTION Brain-computer interface (BCI) is a communication device that allows individuals to control computers or other devices using their brain signals, thereby enabling communication or control without the need for muscle movement [1], [2]. The primary objective of research and advancement in the field of BCIs is to develop an innovative communication tool that can be used specifically for people who have experienced stroke, neurological disorders, muscle impairment, or spinal cord injuries. These individuals face significant challenges in effectively engaging with their environment using conventional communication methods, necessitating the development of alternative means to facilitate their communication capabilities. BCIs enable individuals to issue commands directly to a computer, robot, or smart prosthesis through their brain signals. The important application of BCI performed by machine learning (ML) is device control such as wheelchairs [3], robots [4], car [5], cursor [6], and spellers [7]. Various techniques are used to measure brain activity in BCIs, predominantly relying on the acquisition of electrical signals via invasive or non-invasive approaches. Invasive methods involve the insertion of