# Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2609.22088 · HTML: https://mlsanity.com/paper/2609.22088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [The Value-Improvement Path: Towards Better Representations for Reinforcement Learning](https://mlsanity.com/paper/2006.02243/) (2020)
- [Representations for Stable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2007.05520/) (2020)
- [A Transformer-based deep neural network model for SSVEP classification](https://mlsanity.com/paper/2210.04172/) (2022)
- [Approximate Policy Iteration with Bisimulation Metrics](https://mlsanity.com/paper/2202.02881/) (2022)
- [MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement Learning](https://mlsanity.com/paper/2409.02714/) (2024)
- [Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control](https://mlsanity.com/paper/2603.25968/) (2026)
- [PR-PL: A Novel Transfer Learning Framework with Prototypical Representation based Pairwise Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2202.06509/) (2022)
- [PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditions](https://mlsanity.com/paper/2412.00082/) (2024)
- [A Spatial-Spectral and Temporal Dual Prototype Network for Motor Imagery Brain-Computer Interface](https://mlsanity.com/paper/2407.03177/) (2024)

## Abstract (arXiv)

Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision-making (ATDM) addresses this accuracy-time trade-off by progressively accumulating EEG evidence and deciding when to stop. However, existing EEG encoders are mainly designed for fixed-window decoding and may not provide reliable state representations under variable observation lengths. In addition, current ATDM-oriented encoders are typically tailored to specific EEG paradigms, limiting their applicability across different BCI tasks. To address these limitations, we propose ProtoTrigger, a two-stage prototype learning-based EEG state encoder for ATDM. ProtoTrigger uses prototype matching to extract stable local EEG embeddings and prototype-based attention to aggregate decision-relevant temporal evidence during progressive observation. Offline evaluations across three EEG paradigms demonstrated state-of-the-art accuracy-time trade-offs and strong generalizability across different EEG paradigms. An online human-in-the-loop augmented reality-based BCI experiment further demonstrated its real-time feasibility. These results suggest that ProtoTrigger provides a general EEG state encoding framework for efficient ATDM-based BCI systems.