On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks

TL;DR

This paper introduces a novel CNN-based method for classifying raw dry-EEG signals in SSVEP-based BCI applications, achieving high accuracy without data pre-processing and demonstrating strong generalization across subjects.

Contribution

The paper presents an end-to-end CNN architecture specifically designed for classifying dry-EEG signals in SSVEP paradigms, outperforming traditional methods and other neural network variants.

Findings

Achieved 96% classification accuracy on SSVEP dry-EEG data.

Demonstrated superior cross-subject generalization.

Outperformed traditional machine learning approaches.

Abstract

In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effectiveness of our approach, we utilise the Steady State Visual Evoked Potential (SSVEP) paradigm as our use case. SSVEP can be utilised to allow people with severe physical disabilities such as Complete Locked-In Syndrome or Amyotrophic Lateral Sclerosis to be aided via BCI applications, as it requires only the subject to fixate upon the sensory stimuli of interest. Here we utilise SSVEP flicker frequencies between 10 to 30 Hz, which we record as subject cortical waveforms via the dry-EEG headset. Our proposed end-to-end CNN allows us to automatically and accurately classify SSVEP stimulation directly from the dry-EEG waveforms. Our CNN architecture utilises a common SSVEP Convolutional Unit (SCU), comprising of a 1D convolutional layer, batch normalization and max pooling. Furthermore, we compare several deep learning neural network variants with our primary CNN architecture, in addition to traditional machine learning classification approaches. Experimental evaluation shows our CNN architecture to be significantly better than competing approaches, achieving a classification accuracy of 96% whilst demonstrating superior cross-subject performance and even being able to generalise well to unseen subjects whose data is entirely absent from the training process.