Ceolini相关文献与解析，生知库 | 链接生命科学的每一步

Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions

智能手机触摸屏交互中捕捉到的与年龄相关的行为改变的时间聚集

期刊:iScience

影响因子:4.1

doi:10.1016/j.isci.2022.104791

Ceolini, Enea; Kock, Ruchella; Band, Guido P H; Stoet, Gijsbert; Ghosh, Arko

发表日期：2022

文献求助收藏

Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy

基于智能手机行为训练的人工神经网络可以追踪癫痫中的癫痫样活动

期刊:iScience

影响因子:4.1

doi:10.1016/j.isci.2021.102538

Duckrow, Robert B; Ceolini, Enea; Zaveri, Hitten P; Brooks, Cornell; Ghosh, Arko

Brain-informed speech separation (BISS) for enhancement of target speaker in multitalker speech perception

基于大脑信息的语音分离（BISS）用于增强多说话人语音感知中的目标说话人

期刊:Neuroimage

影响因子:4.5

doi:10.1016/j.neuroimage.2020.117282

Ceolini, Enea; Hjortkjær, Jens; Wong, Daniel D E; O'Sullivan, James; Raghavan, Vinay S; Herrero, Jose; Mehta, Ashesh D; Liu, Shih-Chii; Mesgarani, Nima

神经科学

发表日期：2020

文献求助收藏

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

听觉注意解码的前向模型和后向模型中正则化方法的比较

期刊:Frontiers in Neuroscience

影响因子:3.2

doi:10.3389/fnins.2018.00531

Wong, Daniel D E; Fuglsang, Søren A; Hjortkjær, Jens; Ceolini, Enea; Slaney, Malcolm; de Cheveigné, Alain

发表日期：2018

文献求助收藏

Canadian College of Neuropsychopharmacology 2024 award winners

加拿大神经精神药理学会2024年获奖者

Common multi-day rhythms in smartphone behavior

智能手机行为中常见的多日节律

Neural processing of goal and non-goal-directed movements on the smartphone

智能手机上目标导向和非目标导向运动的神经处理

Temporal clusters of age-related behavioral alterations captured in smartphone touchscreen interactions

智能手机触摸屏交互中捕捉到的与年龄相关的行为改变的时间聚集

Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy

基于智能手机行为训练的人工神经网络可以追踪癫痫中的癫痫样活动

Brain-informed speech separation (BISS) for enhancement of target speaker in multitalker speech perception

基于大脑信息的语音分离（BISS）用于增强多说话人语音感知中的目标说话人

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

听觉注意解码的前向模型和后向模型中正则化方法的比较