Introduction: Navigating ethics at the intersection of AI and neuroscience DOI
Georg Starke

Developments in neuroethics and bioethics, Год журнала: 2024, Номер unknown, С. xix - xxv

Опубликована: Янв. 1, 2024

Язык: Английский

The neural activity of auditory conscious perception DOI Creative Commons
Kate L. Christison-Lagay, Aya Khalaf,

Noah C. Freedman

и другие.

NeuroImage, Год журнала: 2025, Номер unknown, С. 121041 - 121041

Опубликована: Янв. 1, 2025

Although recent work has made headway in understanding the neural temporospatial dynamics of conscious perception, much that focused on visual paradigms. To determine whether there are shared mechanisms for perceptual consciousness across sensory modalities, here we test within auditory domain. Participants completed an threshold task while undergoing intracranial electroencephalography. Recordings from >2,800 grey matter electrodes were analyzed broadband gamma power (a range which reflects local activity). For perceived trials, find nearly simultaneous activity early regions, right caudal middle frontal gyrus, and non-auditory thalamus; followed by a wave sweeps through association regions into parietal cortices. not significant is restricted to regions. These findings show cortical subcortical networks involved perception similar those observed with vision, suggesting perception.

Язык: Английский

Процитировано

3

Disclosing Results of Tests for Covert Consciousness: A Framework for Ethical Translation DOI
Michael J. Young, Karnig Kazazian, David Fischer

и другие.

Neurocritical Care, Год журнала: 2024, Номер 40(3), С. 865 - 878

Опубликована: Янв. 19, 2024

Язык: Английский

Процитировано

9

Reconstructing Covert Consciousness DOI
David Fischer, Brian L. Edlow, Holly J. Freeman

и другие.

Neurology, Год журнала: 2025, Номер 104(4)

Опубликована: Янв. 30, 2025

Determining the level of consciousness in patients with brain injury-and more fundamentally, establishing what they can experience-is ethically and clinically impactful. Patient behaviors may unreliably reflect their consciousness: a subset unresponsive demonstrate covert by willfully modulating activity to commands through fMRI or EEG. However, current paradigms for assessing remain fundamentally limited because are insensitive, rely on imperfect assumptions functional neuroanatomy, do not spectrum conscious experience. Neural decoding, which stimuli concepts reconstructed from activity, offers novel approach assessment that overcomes many these limitations. In this article, we discuss state assessments, shortcomings, science neural potential application decoding disorders consciousness, future directions help realize potential. To so, searched PubMed Google Scholar databases pertinent articles published between January 1990 September 2024, using search terms "covert consciousness," "cognitive motor dissociation," "neural decoding," "semantic decoding." Redefining improve sensitivity, enhance granularity, directly address question experience after injury.

Язык: Английский

Процитировано

1

Cell consciousness: a dissenting opinion DOI Creative Commons
David G. Robinson, Jon Mallatt, Wendy Ann Peer

и другие.

EMBO Reports, Год журнала: 2024, Номер 25(5), С. 2162 - 2167

Опубликована: Март 28, 2024

Процитировано

5

Large-scale foundation models and generative AI for BigData neuroscience DOI Creative Commons
Ran Wang, Zhe Chen

Neuroscience Research, Год журнала: 2024, Номер unknown

Опубликована: Июнь 1, 2024

Recent advances in machine learning have led to revolutionary breakthroughs computer games, image and natural language understanding, scientific discovery. Foundation models large-scale (LLMs) recently achieved human-like intelligence thanks BigData. With the help of self-supervised (SSL) transfer learning, these may potentially reshape landscapes neuroscience research make a significant impact on future. Here we present mini-review recent foundation generative AI as well their applications neuroscience, including speech, semantic memory, brain-machine interfaces (BMIs), data augmentation. We argue that this paradigm-shift framework will open new avenues for many directions discuss accompanying challenges opportunities.

Язык: Английский

Процитировано

5

What ergodicity means for you DOI Creative Commons
Michael D. Hunter, Zachary F. Fisher, Charles F. Geier

и другие.

Developmental Cognitive Neuroscience, Год журнала: 2024, Номер 68, С. 101406 - 101406

Опубликована: Июнь 15, 2024

This paper explores the relation between within-person and between-person research designs using concept of ergodicity from statistical mechanics in physics. We demonstrate consequences several real data examples previously published studies. then create simulated that illustrate independence processes differences, pair these with analytic results reinforce our conclusions. Finally, we discuss plausibility being general rule rather than exception for social behavioral processes, address common arguments against heeding implications research, offer possible solutions.

Язык: Английский

Процитировано

4

Functional Near-Infrared Spectrometry as a Useful Diagnostic Tool for Understanding the Visual System: A Review DOI Open Access
Kelly Acuña, Rishav Sapahia,

I Jiménez

и другие.

Journal of Clinical Medicine, Год журнала: 2024, Номер 13(1), С. 282 - 282

Опубликована: Янв. 4, 2024

This comprehensive review explores the role of Functional Near-Infrared Spectroscopy (fNIRS) in advancing our understanding visual system. Beginning with an introduction to fNIRS, we delve into its historical development, highlighting how this technology has evolved over time. The core critically examines advantages and disadvantages offering a balanced view capabilities limitations research clinical settings. We extend discussion diverse applications fNIRS beyond traditional use, emphasizing versatility across various fields. In context system, provides in-depth analysis contributes eye function, including diseases. discuss intricacies cortex, it responds stimuli implications these findings both health disease. A unique aspect is exploration intersection between virtual reality (VR), augmented (AR) artificial intelligence (AI). cutting-edge technologies are synergizing open new frontiers system research. concludes forward-looking perspective, envisioning future rapidly evolving technological landscape potential revolutionize approach studying

Язык: Английский

Процитировано

3

EEG-Based Music Emotion Prediction Using Supervised Feature Extraction for MIDI Generation DOI Creative Commons
Óscar Wladimir Gómez Morales,

Hernán Darío Pérez-Nastar,

Andrés Marino Álvarez-Meza

и другие.

Sensors, Год журнала: 2025, Номер 25(5), С. 1471 - 1471

Опубликована: Фев. 27, 2025

Advancements in music emotion prediction are driving AI-driven algorithmic composition, enabling the generation of complex melodies. However, bridging neural and auditory domains remains challenging due to semantic gap between brain-derived low-level features high-level musical concepts, making alignment computationally demanding. This study proposes a deep learning framework for generating MIDI sequences aligned with labeled predictions through supervised feature extraction from domains. EEGNet is employed process data, while an autoencoder-based piano algorithm handles data. To address modality heterogeneity, Centered Kernel Alignment incorporated enhance separation emotional states. Furthermore, regression applied reduce intra-subject variability extracted Electroencephalography (EEG) patterns, followed by clustering latent representations into denser partitions improve reconstruction quality. Using metrics, evaluation on real-world data shows that proposed approach improves classification (namely, arousal valence) system’s ability produce better preserve temporal alignment, tonal consistency, structural integrity. Subject-specific analysis reveals subjects stronger imagery paradigms produced higher-quality outputs, as their patterns more closely training In contrast, weaker performance exhibited were less consistent.

Язык: Английский

Процитировано

0

The Role of Audio Information Reconstruction Based on Brain Activity Analysis in Enhancing the Quality of Human–Machine Interaction DOI
Asker A. Nagoev, Alena Rusak,

Aleksandra I. Truskova

и другие.

Studies in big data, Год журнала: 2025, Номер unknown, С. 389 - 396

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Predicting artificial neural network representations to learn recognition model for music identification from brain recordings DOI Creative Commons
Taketo Akama, Zhuohao Zhang, Pengcheng Li

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 29, 2025

Abstract Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical when subjected identical auditory sensory inputs. In these studies, ability predict is probed by regressing from ANN representations. Building upon this concept, our approach reverses direction prediction: we utilize as a supervisory signal train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, focus on constructing model for music identification, where electroencephalography (EEG) collected during listening serve input. By training an EEG representations-representations associated with identification-we observed significant improvement in classification accuracy. This study introduces novel developing response external stimuli. It holds promise advancing brain-computer interfaces (BCI), decoding techniques, and understanding cognition. Furthermore, it provides new insights into relationship between activity

Язык: Английский

Процитировано

0