Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

2025-03-07

AI Summary

To address the challenge of decoding EEG signals across heterogeneous EEG headsets—differing in electrode count and spatial layout—this paper proposes a robust, target-label-free cross-device transfer method. The method introduces spatial knowledge distillation into cross-device transfer for the first time and establishes a three-level joint distribution alignment framework—spanning input, feature, and output spaces—integrated with multi-layer Maximum Mean Discrepancy (MMD) and Wasserstein distance metrics, a deep EEG encoder, and a cross-paradigm domain adaptation training strategy. Extensive evaluation is conducted across six public EEG datasets and two major BCI paradigms: motor imagery (MI) and event-related potentials (ERP). The approach achieves state-of-the-art performance in both offline unsupervised and online supervised settings, significantly outperforming ten baseline methods.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.

Problem

• Addresses EEG signal decoding across different headsets
• Proposes SDDA for heterogeneous cross-headset transfer
• Improves performance in domain adaptation scenarios

Innovation

• Spatial distillation for electrode utilization
• Distribution alignment for domain adaptation
• Knowledge distillation in cross-headset transfers