Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
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 …
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 for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art
Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
A benchmark dataset for SSVEP-based brain–computer interfaces
This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset
acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64 …
acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64 …
MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation
N Driscoll, B Erickson, BB Murphy… - Science translational …, 2021 - science.org
Soft bioelectronic interfaces for mapping and modulating excitable networks at high
resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and …
resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and …
Review of the BCI competition IV
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide
high quality neuroscientific data for open access to the scientific community. As experienced …
high quality neuroscientific data for open access to the scientific community. As experienced …
Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms
One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is
common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is …
common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is …
Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification
B Xu, L Zhang, A Song, C Wu, W Li, D Zhang… - Ieee …, 2018 - ieeexplore.ieee.org
Feature extraction and classification play an important role in brain–computer interface (BCI)
systems. In traditional approaches, methods in pattern recognition field are adopted to solve …
systems. In traditional approaches, methods in pattern recognition field are adopted to solve …