A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification

TL;DR

This paper introduces DADL-Net, a deep learning model that enhances EEG-based motor imagery classification by capturing spatial-temporal features and adapting dynamically across subjects and sessions, reducing calibration time.

Contribution

The paper proposes a novel deep learning framework with dynamic domain adaptation for EEG motor imagery classification, addressing inter-individual and session variability.

Findings

Achieved 70.42% accuracy on OpenBMI dataset.

Achieved 73.91% accuracy on BCIC IV 2a dataset.

Demonstrated effectiveness of domain adaptation in EEG classification.

Abstract

There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. In order to solve the above problems, we propose a Dynamic Domain Adaptation Based Deep Learning Network (DADL-Net). First, the EEG data is mapped to the three-dimensional geometric space and its temporal-spatial features are learned through the 3D convolution module, and then the spatial-channel attention mechanism is used to strengthen the features, and the final convolution module can further learn the spatial-temporal information of the features. Finally, to account for inter-subject and cross-sessions differences, we employ a dynamic domain-adaptive strategy, the distance between features is reduced by introducing a Maximum Mean Discrepancy loss function, and the classification layer is fine-tuned by using part of the target domain data. We verify the performance of the proposed method on BCI competition IV 2a and OpenBMI datasets. Under the intra-subject experiment, the accuracy rates of 70.42% and 73.91% were achieved on the OpenBMI and BCIC IV 2a datasets.