Brain-computer interfaces (BCIs) hold great potential for aiding individuals with speech impairments. Utilizing electroencephalography (EEG) to decode speech is particularly promising due to its non-invasive nature. However, recordings are typically short, and the high variability in EEG data has led researchers to focus on classification tasks with a few dozen classes. To assess its practical applicability for speech neuroprostheses, we investigate the relationship between the size of EEG data and decoding accuracy in the open vocabulary setting. We collected extensive EEG data from a single participant (175 hours) and conducted zero-shot speech segment classification using self-supervised representation learning. The model trained on the entire dataset achieved a top-1 accuracy of 48\% and a top-10 accuracy of 76\%, while mitigating the effects of myopotential artifacts. Conversely, when the data was limited to the typical amount used in practice ($\sim$10 hours), the top-1 accuracy dropped to 2.5\%, revealing a significant scaling effect. Additionally, as the amount of training data increased, the EEG latent representation progressively exhibited clearer temporal structures of spoken phrases. This indicates that the decoder can recognize speech segments in a data-driven manner without explicit measurements of word recognition. This research marks a significant step towards the practical realization of EEG-based speech BCIs.
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆电子工程职业大学智能健康学院）
Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；RIKEN Center for Advanced Intelligence Project（日本理化学研究所先进智能项目中心）；RIKEN（日本理化学研究所）；Systems Research Institute（系统研究所）；Polish Academy of Sciences（波兰科学院）；Nicolaus Copernicus University（尼古拉哥尔斯基大学）；University of Rome Sapienza（罗马萨皮恩扎大学）
Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks
Schmid College of Science and Technology, Chapman University（施密特科学与技术学院，查普曼大学）；LUCID-AI, Chapman University（LUCID-AI，查普曼大学）；Helsinki Institute of Life Science, University of Helsinki（赫尔辛基生命科学研究所，赫尔辛基大学）；Neuroscience Center, University of Helsinki（神经科学中心，赫尔辛基大学）；Faculty of Pharmacy, University of Helsinki（药学院，赫尔辛基大学）；Crean College of Health and Behavioral Sciences, Chapman University（克里安健康与行为科学学院，查普曼大学）；Department of Biology and Bioengineering, California Institute of Technology（生物学与生物工程系，加州理工学院）；Anderson School of Management, University of California Los Angeles（安德森管理学院，加州大学洛杉矶分校）
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment
Department of Psychology, The Ohio State University（心理学系，俄亥俄州立大学）；McGovern Institute for Brain Research, Massachusetts Institute of Technology（脑科学研究所，麻省理工学院）；Department of Neuroscience, The University of Texas at Dallas（神经科学系，德克萨斯大学达拉斯分校）
