A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network

TL;DR

This paper introduces a two-stream CNN hybrid BCI that combines SSVEP and motor imagery paradigms, automatically extracts features, and significantly improves decoding accuracy over single-mode systems.

Contribution

It presents a novel TSCNN model that fuses SSVEP and MI EEG signals, enhancing decoding accuracy and versatility in single and hybrid modes.

Findings

Decoding accuracy improved by 25.4% over MI mode

Achieved 93.0% accuracy in SSVEP mode

Hybrid mode accuracy reached 95.6%

Abstract

The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms. TSCNN automatically learns to extract EEG features in the two paradigms in the training process, and improves the decoding accuracy by 25.4% compared with the MI mode, and 2.6% compared with SSVEP mode in the test data. Moreover, the versatility of TSCNN is verified as it provides considerable performance in both single-mode (70.2% for MI, 93.0% for SSVEP) and hybrid-mode scenarios (95.6% for MI-SSVEP hybrid). Our work will facilitate the real-world applications of EEG-based BCI systems.