Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network
Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community
to find evidence of cortical involvement at walking initiation and during locomotion. However …
to find evidence of cortical involvement at walking initiation and during locomotion. However …
Data-driven models in human neuroscience and neuroengineering
Highlights•Data-intensive discovery is increasingly prevalent in modern human
neuroscience.•Well-motivated choices of input and output data is crucial in data-driven …
neuroscience.•Well-motivated choices of input and output data is crucial in data-driven …
Online asynchronous decoding of error-related potentials during the continuous control of a robot
Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the
detection of ErrPs is an intuitive approach to improve the performance of brain-computer …
detection of ErrPs is an intuitive approach to improve the performance of brain-computer …
[PDF][PDF] Metabolism of methylenedioxymethamphetamine: formation of dihydroxymethamphetamine and a quinone identified as its glutathione adduct.
M Hiramatsu, Y Kumagai, SE Unger, AK Cho - Journal of Pharmacology …, 1990 - Citeseer
The in vitro conversion of (+)-3, 4-methylenedioxymethamphe-tamine and (-)-3, 4-
methylenedioxymethamphetamine to the corresponding catecholamine, 3, 4 …
methylenedioxymethamphetamine to the corresponding catecholamine, 3, 4 …
Diffusion property and functional connectivity of superior longitudinal fasciculus underpin human metacognition
Metacognition as the capacity of monitoring one's own cognition operates across domains.
Here, we addressed whether metacognition in different cognitive domains rely on common …
Here, we addressed whether metacognition in different cognitive domains rely on common …
Development of LSTM&CNN based hybrid deep learning model to classify motor imagery tasks
C Uyulan - bioRxiv, 2020 - biorxiv.org
Recent studies underline the contribution of brain-computer interface (BCI) applications to
the enhancement process of the life quality of physically impaired subjects. In this context, to …
the enhancement process of the life quality of physically impaired subjects. In this context, to …
[CITATION][C] Decoupled weight decay regularization
I Loshchilov - arxiv preprint arxiv:1711.05101, 2017
Interpretable functional specialization emerges in deep convolutional networks trained on brain signals
Objective. Functional specialization is fundamental to neural information processing. Here,
we study whether and how functional specialization emerges in artificial deep convolutional …
we study whether and how functional specialization emerges in artificial deep convolutional …
Hybrid brain-computer-interfacing for human-compliant robots: inferring continuous subjective ratings with deep regression
Appropriate robot behavior during human-robot interaction is a key part in the development
of human-compliant assistive robotic systems. This study poses the question of how to …
of human-compliant assistive robotic systems. This study poses the question of how to …