A Single-Channel SSVEP-Based BCI Speller Using Deep Learning

This paper aims to develop a speller system based on a bipolar single-channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom-designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five-target steady-state visual-evoked potential (SSVEP)-based brain–computer interface (BCI) utilizing one-dimensional convolutional neural network (1-D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time-window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five-class SSVEP data with a high accuracy of 99.2%. The proposed BCI is then employed in an online experiment of spelling the word “SPELLER” using 2-s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 ± 7.7 bpm, showing the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller utilizing 1-D CNN.