neural-decoding

The paper proposes EEG-AS, an algorithm selection framework that enables instance-level selection among multiple EEG foundation models by reconstructing their behaviors, thereby improving neural decoding performance.

The paper introduces LibriBrain100, a large-scale MEG dataset with over 100 hours of data for neural speech decoding, achieving state-of-the-art performance on word classification benchmarks.

This paper introduces the Von-Neumann State-Space Transformer (VN-SST), a new model inspired by von-Neumann architecture that improves sample efficiency in neural decoding by using a low-rank instruction bank for token-specific operations, outperforming standard Transformers on benchmarks.

This research uses non-invasive EEG and contrastive learning to decode words during silent reading, showing scalable lexical information recovery that scales with data volume.

The paper proposes BLPM, an EEG-language foundation model that uses continuous latent predictive modeling and semantic alignment to map EEG signals to text embeddings, achieving generalizable neural decoding across diverse tasks and datasets.

NeuroPB is a framework that scales neural decoding by pretraining a motor encoder on large-scale behavioral data (including robotic trajectories) and aligning neural activity to that representation space, improving trajectory decoding and generalization with limited neural data.

This paper presents an interpretable MEG-to-audio retrieval model for perceived speech, redesigned with spherical-harmonics spatial attention and source mapping, achieving 39.75% Top-1 accuracy with far fewer decoder parameters while revealing which speech features drive retrieval.

This paper improves the Huth encoding pipeline for fMRI decoding and introduces fMRIFlamingo, a direct fMRI-to-text framework using Llama 3.2, but finds that decoding success is driven by the language prior rather than neural input.

This paper introduces a multi-feature fusion framework for semantic reconstruction from non-invasive brain recordings, combining static lexical (Word2Vec) and dynamic contextual (GPT) representations via cross-attention, achieving state-of-the-art performance in brain-to-text decoding.

Brain2Qwerty is a non-invasive brain-computer interface that decodes brain waves into text, enabling communication without surgery.

NeuroSonic introduces a conditional flow-matching framework for reconstructing continuous speech from EEG signals, addressing the structural mismatch between neural and acoustic data by learning a deterministic probability-flow velocity field. It achieves up to 26.3% improvement in perceptual quality over existing GAN, diffusion, and mean-flow baselines on cross-subject benchmarks.

This paper applies TopK Sparse Autoencoders to three EEG foundation models (SleepFM, REVE, LaBraM) to extract interpretable feature dictionaries and introduces a framework for concept steering, revealing representational failures and clinical entanglements.

Researchers propose Brain-CLIPLM, a two-stage EEG-to-text decoding framework using contrastive learning for semantic anchor extraction and a retrieval-grounded LLM with Chain-of-Thought reasoning, achieving 67.55% top-5 sentence retrieval accuracy and suggesting EEG-to-text decoding should focus on recovering compressed semantic content rather than full sentence reconstruction.