Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks

TL;DR

This study explores the use of convolutional neural networks to classify EEG signals related to visual perception and imagery, demonstrating promising accuracy for BCI control and differentiation between paradigms.

Contribution

The paper introduces a CNN-based approach for classifying VP and VI EEG signals, showing improved differentiation and potential for BCI applications.

Findings

6-class VP classification accuracy: 32.56%

Binary VP/VI classification accuracy: 90.16%

Time-frequency analysis supports signal distinguishability

Abstract

Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information, respectively. These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation. In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56% and the binary classification performance which classifies two paradigms was 90.16%.