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New AI-based method reduces latency in neurofeedback by 50-fold

Researchers from HSE University and the Artificial Intelligence Research Institute have successfully lowered the latency between a change in brain activity and the presentation of the corresponding neurofeedback signal by a factor of 50. ....

Lily Ramsey , Alexei Ossadtchi , Head Of The Neurointerfaces Group , Centre For Bioelectric Interfaces , Artificial Intelligence Research Institute , Temporal Convolutional Network , Research Team Leader , University Institute For Cognitive Neuroscience , Bioelectric Interfaces , Cognitive Neuroscience , Neurointerfaces Group , Artificial Intelligence , Attention Deficit Disorder ,

Neuroscientists use neural network to enhance neurofeedback technology

Neuroscientists use neural network to enhance neurofeedback technology
medicalxpress.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from medicalxpress.com Daily Mail and Mail on Sunday newspapers.

Alexei Ossadtchi , Ilia Semenkov , Centre For Bioelectric Interfaces , University Institute For Cognitive Neuroscience , Temporal Convolutional Network , Research Team Leader , Artificial Intelligence Research Institute , Head Of The Neurointerfaces Group , Bioelectric Interfaces , Cognitive Neuroscience , Neurointerfaces Group , Neural Engineering ,

HSE Neuroscientists Boost Neurofeedback Tech with Neural Network

HSE Neuroscientists Boost Neurofeedback Tech with Neural Network
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Alexei Ossadtchi , University Institute For Cognitive Neuroscience , Temporal Convolutional Network , Artificial Intelligence Research Institute , Centre For Bioelectric Interfaces , Research Team Leader , Head Of The Neurointerfaces Group , Bioelectric Interfaces , Cognitive Neuroscience , Neurointerfaces Group ,

"Multi-features fusion for short-term photovoltaic power prediction" by Ming Ma, Xiaorun Tang et al.

In recent years, in order to achieve the goal of 'carbon peaking and carbon neutralization', many countries have focused on the development of clean energy, and the prediction of photovoltaic power generation has become a hot research topic. However, many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation, and they rarely consider the multi-features fusion methods for power prediction. This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China, and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation. Next, the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets. Finally, traditional time series prediction methods, such as Recurrent Neural Network (RNN), Convolution Neural Network ....

Temporal Convolutional Network , Recurrent Neural Network , Convolution Neural Network , Northwest China , Extreme Gradient Boosting , Long Short Term Memory , Meteorological Factors , Ulti Features Fusion , Photovoltaic Power Prediction , Time Series Prediction ,