Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering

TL;DR

This paper introduces a user state-aware EEG filtering framework that improves BCI decoding stability by adaptively filtering noisy segments based on estimated user attention, enhancing long-term robustness.

Contribution

It presents a novel adaptive filtering method that estimates user cognitive states from EEG to improve BCI decoding stability without extra user labels.

Findings

Enhanced classification accuracy across sessions.

Improved decoding stability under varying user states.

Effective suppression of noisy EEG segments.

Abstract

Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (EEG) filtering framework that refines neural representations before decoding user intentions. The proposed method continuously estimates the user's cognitive state (e.g., focus or distraction) from EEG features and filters unreliable segments by applying adaptive weighting based on the estimated attention level. This filtering stage suppresses noisy or out-of-focus epochs, thereby reducing distributional drift and improving the consistency of subsequent decoding. Experiments on multiple EEG datasets that emulate real BCI scenarios demonstrate that the proposed state-aware filtering enhances classification accuracy and stability across different user states and sessions compared with conventional preprocessing pipelines. These findings highlight that leveraging brain-derived state information--even without additional user labels--can substantially improve the reliability of practical EEG-based BCIs.