MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data
HiTZ Center, University of the Basque Country -- UPV/EHU, Spain（比斯开国家大学HiTZ中心）；Basque Center on Cognition, Brain and Language -- BCBL, Spain（巴斯克认知、大脑与语言中心）；Ikerbasque, Basque Foundation for Science, Spain（巴斯克科学基金会Ikerbasque）；Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB), Belgium（布鲁塞尔自由大学人体运动科学系功能解剖实验室）；Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN2T), ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Belgium（布鲁塞尔自由大学神经解剖与神经影像翻译实验室）；WEL Research Institute, Belgium（比利时WEL研究机构）
Xabier de Zuazo, Vincenzo Verbeni, Eva Navas, Ibon Saratxaga, Mathieu Bourguignon, Nicola Molinaro
Data-efficient neural decoding is a central challenge for speech brain-computer interfaces. We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production. We pre-train a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants. Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%. Not only does pre-training improve performance within each task, but it also enables reliable cross-task decoding between perception and production. Critically, models trained on speech production decode passive listening above chance, confirming that learned representations reflect shared neural processes rather than task-specific motor activity.
Decoding Phone Pairs from MEG Signals Across Speech Modalities
HiTZ Center, University of the Basque Country -- UPV/EHU（巴斯克国家大学希茨中心）；Basque Centre on Cognition, Brain and Language -- BCBL（巴斯克认知、大脑与语言中心）；Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB)（人类运动科学系功能解剖实验室）；Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN2T), ULB Neuroscience Institute, Université libre de Bruxelles (ULB)（神经解剖与神经影像翻译实验室（LN2T）、ULB神经科学研究所）；WEL Research Institute（WEL研究机构）
Speech Recognition: Keyword Spotting Through Image Recognition
Institute for Clarity in Documentation（文档清晰度研究所）；Brookhaven Laboratories（布鲁克海文实验室）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Palmer Research Laboratories（帕尔默研究实验室）
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；University of Chinese Academy of Sciences（中国科学院大学）
