A Deep Neural Network for SSVEP-based Brain-Computer Interfaces

TL;DR

This paper introduces a novel deep neural network architecture for SSVEP-based brain-computer interfaces that significantly improves target identification accuracy and information transfer rates, demonstrating state-of-the-art performance on large datasets.

Contribution

The paper presents a new DNN architecture that processes multi-channel SSVEP signals with convolutional layers across harmonics, channels, and time, achieving superior classification performance.

Findings

Achieved record-high ITRs of 265.23 and 196.59 bits/min on two datasets.

Outperformed existing state-of-the-art methods in accuracy and speed.

Demonstrated robustness across large-scale datasets with 105 subjects.

Abstract

Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture. Method: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities. Results: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI. Conclusion: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets. Significance: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.