15 papers · ranked by Valyu relevance
Stéphane d’Ascoli, Corentin Bel, Jérémy Rapin, Hubert Banville + 3 more
While deep learning has enabled the decoding of language from intracranial brain recordings, achieving this with non-invasive recordings remains an open challenge. We introduce a deep learning pipeline to decode individual words from electro- (EEG) and magneto-encephalography (MEG) signals. We evaluate our approach on…
Joshua P. Chu, Michael E. Coulter, Eric L. Denovellis, Trevor Thai K. Nguyen + 5 more
Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing…
Xin-Ya Zhang, Hang Lin, Zeyu Deng, Markus Siegel + 2 more
Artificial neural networks (ANNs) offer a data-driven approach to reveal brain regional functions without explicit supervision. Here, we demonstrate that an ANN trained to decode visual stimuli from multi-unit spiking activity in monkeys, can not only reconstruct complex and dynamic scenes, but also spontaneously align…
Sheng Wang, Xiaobin Song, Xiaopan Song, Yang Gu + 3 more
Title: Highlights 1. The latest advancements in neural signal decoding and the integration of flexible bioelectronics for non-invasive brain-computer interfaces are reviewed. 2. Multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies are critical for enhancing the robustness…
Cedric Foucault, Tiffany Bounmy, Sébastien Demortain, Bertrand Thirion + 2 more
Assessing probabilities and predicting future events are fundamental for perception and adaptive behavior, yet the neural representations of probability remain elusive. While previous studies have shown that neural activity in several brain regions correlates with probability-related factors such as surprise and…
Robert Worden
This paper uses simple arguments to derive a negative conclusion: that a computer cannot be conscious. If the brain is only a neural computer, brains cannot be conscious. Consciousness implies that there is something else happening in the brain, besides computation. In a running computer, information about outside…
Esra Sümer-Arpak, Rajkumar Saini, Debashis Das Chakladar, Sanjeev Kumar Varun + 1 more
Inner speech (IS), or imagined speech without overt articulation, is a promising target for brain-computer interfaces (BCIs) aimed at restoring communication in individuals with severe speech impairments, such as locked-in syndrome. Foundation models (FMs), typically trained using self-supervised learning (SSL) on…
Hee Kyu Lee, Hyun Bin Kim, Sang Uk Park, Janghoon Joo + 6 more
Brain-computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode-tissue interface, motion-induced artifacts, inter-user…
Hamid Karimi-Rouzbahani, Anina N. Rich, Alexandra Woolgar
The multiple-demand network (MDN), a set of highly interconnected, domain-general regions active across a wide variety of cognitively demanding tasks, is thought to support cognitive functions by integrating distinct types of information depending on the task. However, the spatiotemporal characteristics with which each…
Lingwei Zeng, Wanying Xing, Di Wu, Minghao Dong + 3 more
Significance Traditional exposure therapy or cognitive training requires repeated presentation of unwanted stimuli, whereas localizationist neuromodulation overlooks individual variation. We propose a closed-loop neuromodulation approach termed functional near-infrared spectroscopy-decoded neurofeedback training…
Changzeng Liu, Yu Guo, Jin Ding, Ling Li + 3 more
Title: Simple Summary Understanding how the human brain processes and comprehends various sounds remains a major challenge in modern science. Traditional multivariate analysis methods often employ static strategies and overlook the temporal dependencies of neural responses. Therefore, our primary objective was to…
Yaoda Xu, Benjamin Swinchoski, JohnMark Taylor, Marvin Chun
Two defining visual features of a real-world object are its shape and color. While previous fMRI pattern decoding studies showed that these features are represented in a largely orthogonal and independent manner in the human occipitotemporal cortex (OTC) during visual perception, how they are coded together in visual…
Toktam Samiei, Hafiz Fareed Ahmed, Edward Zagha, Erfan Nozari
Despite over a century of research into the neural code, the fundamental principles by which the brain encodes sensory information remain debated. In this study we provide converging evidence for the presence of a dynamic, fast-switching integration of rate and temporal coding in the thalamus, primary visual cortex…
Janet M. Baker, Peter Cariani
Waves are fundamental. In our view, waves in the brain may constitute and drive organized neural activity patterns on individual neural and population levels. Their interactions follow basic physical principles. Taking a comprehensive, temporal and spatiotemporal perspective, we endeavor to explain multiple brain…
Changhao Xiong, Qiang Yang, Sungkean Kim, Sreenivasan Meyyappan + 3 more
Cueing paradigms are commonly used to study the neural mechanisms of visual spatial attention control. In these paradigms, each trial starts with an external cue, which instructs the subject to pay covert attention to a spatial location in anticipation of an impending stimulus (instructed attention). Recent work has…