MVCNet: Multi-View Contrastive Network for Motor Imagery Classification

TL;DR

MVCNet is a dual-branch neural network that combines CNN and Transformer architectures with multi-view contrastive learning to improve motor imagery EEG classification accuracy and generalization across datasets.

Contribution

Introduces MVCNet, a novel multi-view contrastive network with dual-branch architecture and contrastive modules for enhanced MI decoding performance.

Findings

Outperforms nine state-of-the-art MI decoding models.

Demonstrates robustness and generalization across five datasets.

Effectively integrates multi-view information for EEG analysis.

Abstract

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to its intuitive mechanism. However, most existing models rely on single-stream architectures and overlook the multi-view nature of EEG signals, leading to limited performance and generalization. We propose a multi-view contrastive network (MVCNet), a dual-branch architecture that parallelly integrates CNN and Transformer blocks to capture both local spatial-temporal features and global temporal dependencies. To enhance the informativeness of training data, MVCNet incorporates a unified augmentation pipeline across time, frequency, and spatial domains. Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both branches. Final representations are fused and jointly optimized by contrastive and classification losses. Experiments on five public MI datasets across three scenarios demonstrate that MVCNet consistently outperforms nine state-of-the-art MI decoding networks, highlighting its effectiveness and generalization ability. MVCNet provides a robust solution for MI decoding by integrating multi-view information and dual-branch modeling, contributing to the development of more reliable BCI systems.