Improving the Classification of Real-World SSVEP Data in Brain-Computer Interface Speller Systems Using Deep Convolutional Neural Networks

Abstract

Purpose: Brain-Computer Interface (BCI) Speller systems help people with mobility impairments improve their cognitive and physical abilities. Steady-State Visual Evoked Potential (SSVEP) signals have been used to build high-speed BCI speller systems. SSVEP signals are a subtype of Visual Evoked Potential (VEP), a form of co-frequency, and the harmonics response elicited by a specific frequency stimulus. Noise and artifacts are critical issues for target detection in SSVEP-based BCI systems.

Materials and Methods: Thus, it is essential to provide target detection techniques that operate well in the presence of noises. One solution for overcoming the noise impact is to employ approaches that automatically extract the appropriate features for target detection from the training data. Deep Convolutional Neural Network (DCNN) was utilized in this study to automatically extract features from SSVEP data in noisy conditions. Moreover, the BETA database, which contains SSVEP data from 70 individuals collected outside of the electromagnetic shielding room, was used. In this regard, a suitable DCNN structure for target stimulus frequency identification was first designed. The network was pre-trained with part of the data from the BETA database. Finally, at the single-subject level, this pre-trained network was retrained and evaluated.

Results: The results showed that after retraining, the accuracy and Information Transfer Rate (ITR) increased (p-value < 0.01) for all participants.

Conclusion:.The enhancement in accuracy and ITR are 25.72% and 43.10 bpm, respectively.