[HTML][HTML] Decoding movement kinematics from EEG using an interpretable convolutional neural network
Continuous decoding of hand kinematics has been recently explored for the intuitive control
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination
D Borra, S Fantozzi, E Magosso - Neural Networks, 2020 - Elsevier
Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding:
these techniques, by automatically learning relevant features for class discrimination …
these techniques, by automatically learning relevant features for class discrimination …
[HTML][HTML] Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli
Perception of social stimuli (faces and bodies) relies on “holistic”(ie, global) mechanisms, as
supported by picture-plane inversion: perceiving inverted faces/bodies is harder than …
supported by picture-plane inversion: perceiving inverted faces/bodies is harder than …
A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism
D Borra, E Magosso, M Castelo-Branco… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. P300 can be analyzed in autism spectrum disorder (ASD) to derive biomarkers
and can be decoded in brain–computer interfaces to reinforce ASD impaired skills …
and can be decoded in brain–computer interfaces to reinforce ASD impaired skills …
[HTML][HTML] A lightweight multi-scale convolutional neural network for P300 decoding: analysis of training strategies and uncovering of network decision
D Borra, S Fantozzi, E Magosso - Frontiers in Human Neuroscience, 2021 - frontiersin.org
Convolutional neural networks (CNNs), which automatically learn features from raw data to
approximate functions, are being increasingly applied to the end-to-end analysis of …
approximate functions, are being increasingly applied to the end-to-end analysis of …
[HTML][HTML] A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
The application of deep learning methods to raw electroencephalogram (EEG) data is
growing increasingly common. While these methods offer the possibility of improved …
growing increasingly common. While these methods offer the possibility of improved …
Deep learning-based EEG analysis: investigating P3 ERP components
D Borra, E Magosso - Journal of Integrative Neuroscience, 2021 - cris.unibo.it
The neural processing of incoming stimuli can be analysed from the electroencephalogram
(EEG) through event-related potentials (ERPs). The P3 component is largely investigated as …
(EEG) through event-related potentials (ERPs). The P3 component is largely investigated as …
Motor decoding from the posterior parietal cortex using deep neural networks
Objective. Motor decoding is crucial to translate the neural activity for brain-computer
interfaces (BCIs) and provides information on how motor states are encoded in the brain …
interfaces (BCIs) and provides information on how motor states are encoded in the brain …
BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery
X Wang, Y Wang, W Qi, D Kong, W Wang - Neural Networks, 2024 - Elsevier
Brain–computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to
interact with the world through brain signals. To meet demands of real-time, stable, and …
interact with the world through brain signals. To meet demands of real-time, stable, and …