NeuroWeaver is an autonomous evolutionary agent that designs EEG analysis pipelines by framing pipeline engineering as a discrete constrained optimization problem solved with large language model–driven code generation. It uses a Domain‑Informed Subspace Initialization to keep the search within neuroscientifically plausible solutions and a Multi‑Objective Evolutionary Optimization to balance performance, novelty, and efficiency. On five diverse benchmarks, NeuroWeaver produces lightweight pipelines that outperform state‑of‑the‑art task‑specific methods and match or exceed large foundation models while using far fewer parameters.
By Guoan Wang, Shihao Yang, Feng Liu
arXiv:2608. 04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instructions, signal processing, quantitative evidence, and scientific interpretation.
By Yangxuan Zhou, Sha Zhao, Yuning Chen, Chen Wu, Jiquan Wang, Shijian Li, Gang Pan
arXiv:2609.36736v1 Announce Type: cross
Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative in...
By Moein Khajehnejad, Forough Habibollahi, Tommaso Boccato, Margarida Sousa, Michal Olak, Francesco Jamal Sheiban, Matteo Ferrante
NS-Copilot is a large‑language‑model driven multi‑agent system designed to automate neuroscience data analysis. It integrates domain‑specific pre‑trained models for modalities such as EEG and extracellular spike data, and uses a natural‑language interface to orchestrate agents that plan, generate code, and synthesize results. In benchmarks on Alzheimer’s, Parkinson’s, and working‑memory spike decoding, the system consistently outperformed strong baselines across multiple trials.
By Wuche Liu, Yiran Qiao, Linlin Hou, Rui Yang, Shusen Pu, Song Wang, Jing Ma
arXiv:2609.20904v1 Announce Type: cross
Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrop...
By Boyuan Zhao, Sifan Zhang, Luping Chen
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu
arXiv:2606. 26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use.
By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
By Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu, Gourav Datta, Shreyas Sen
arXiv:2606. 18816v1 Announce Type: cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware.
By Gourav Siddhad, Yogesh Kumar Meena
arXiv:2606. 00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions.
By Ziling Lu, Zongsheng Li, Xinke Shen, Kexin Lou, Yingyue Xin, Xiaoqi Chen, Shinan Wang, Xiang Chen, Jiahao Fan, Chenyu Huang, Xin Xu, Zhoujie Hou, Chen Wei, Quanying Liu
arXiv:2607. 29347v1 Announce Type: cross Abstract: Modern neuroscience relies on integrating multi-scale, multimodal datasets to uncover the neural principles underlying intelligence.
By Jiamin Wu, Peishan Xiang, Jingyang Chen, Yuqing Zhu, Yuxi Li, Ling Luo, Qihao Zheng, Jialiang Zu, Yongchao Wu, Mindong Liu, Haitao Wu, Chaofan Hu, Yijie Sun, Yuqi Hang, Yu Zhu, Shuo Li, Yue Fan, Shiyang Feng, Wanghan Xu, Tianlei Zhang, Jie Zhang, Wenlong Zhang, Bo Zhang, Kai Wang, Lei Bai, Mianxin Liu, Wanli Ouyang, Jiulin Du, Chunfeng Song
arXiv:2606. 26519v2 Announce Type: replace Abstract: Large language models (LLMs) can make scientific software easier to use.
By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo