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 in mining biological data
Recent technological advancements in data acquisition tools allowed life scientists to
acquire multimodal data from different biological application domains. Categorized in three …
acquire multimodal data from different biological application domains. Categorized in three …
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of
attention as these signals encode a person's intent of performing an action. Researchers …
attention as these signals encode a person's intent of performing an action. Researchers …
EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
Applications of deep learning and reinforcement learning to biological data
Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …
new possibilities for life scientists to gather multimodal data in various application domains …
Learning temporal information for brain-computer interface using convolutional neural networks
Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …
algorithms for computer vision and natural language processing problems. However, the …
A novel deep learning approach for classification of EEG motor imagery signals
Objective. Signal classification is an important issue in brain computer interface (BCI)
systems. Deep learning approaches have been used successfully in many recent studies to …
systems. Deep learning approaches have been used successfully in many recent studies to …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
A deep learning scheme for motor imagery classification based on restricted Boltzmann machines
Motor imagery classification is an important topic in brain-computer interface (BCI) research
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …
that enables the recognition of a subject's intension to, eg, implement prosthesis control. The …