# Maximizing Single-Feature Separability for Improving Transfer Learning in Motor Imagery EEG Decoding

## TL;DR

This paper introduces a new method called MSFS to improve brain-computer interfaces by enhancing transfer learning in motor imagery EEG decoding.

## Contribution

The novel contribution is the MSFS regularization technique that improves subject-specific EEG decoding using within-dataset transfer learning.

## Key findings

- MSFS consistently improves transfer learning performance across multiple datasets and neural network architectures.
- MSFS remains effective even when the target subject has limited labeled data.
- Ablation studies confirm the effectiveness of MSFS components.

## Abstract

Background/Objectives: Motor imagery (MI) EEG-based brain–computer interfaces (BCIs) are promising for neurorehabilitation, but practical use is often hindered by time-consuming per-user calibration and performance instability across sessions/users. Methods: To mitigate this issue, we aim to improve subject-dependent MI classification by leveraging labeled training data from other subjects within the same dataset via transfer learning. We propose Maximizing Single-Feature Separability (MSFS), a lightweight plug-in regularization applied during target–subject fine-tuning. MSFS operates on the network feature layer and constructs batch-wise target positions by maximizing a silhouette-based separability criterion for each feature dimension. The target position computation is implemented in a fully vectorized GPU-friendly manner. Results: We evaluate MSFS on BCI Competition IV-2a and IV-2b datasets using three representative backbone networks (EEGNet, ShallowConvNet, ATCNet). MSFS consistently improves standard transfer learning across both datasets and all backbones. When compared against representative transfer learning algorithms from the literature, MSFS remains competitive against the literature baselines. Ablation analysis confirms the effectiveness of each algorithm component. Few-shot experiments further indicate that MSFS is still beneficial when the target subject provides limited labeled data. Conclusions: MSFS provides a within-dataset transfer learning enhancement for MI EEG decoding, improving target–subject accuracy under limited calibration data without relying on external datasets, and can be readily integrated into common deep MI classification pipelines.