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 …
Advanced bioelectrical signal processing methods: Past, present and future approach—part i: Cardiac signals
Advanced signal processing methods are one of the fastest developing scientific and
technical areas of biomedical engineering with increasing usage in current clinical practice …
technical areas of biomedical engineering with increasing usage in current clinical practice …
Advanced bioelectrical signal processing methods: Past, present and future approach—Part II: Brain signals
As it was mentioned in the previous part of this work (Part I)—the advanced signal
processing methods are one of the quickest and the most dynamically developing scientific …
processing methods are one of the quickest and the most dynamically developing scientific …
Motor imagery EEG classification based on flexible analytic wavelet transform
Y You, W Chen, T Zhang - Biomedical Signal Processing and Control, 2020 - Elsevier
Motor imagery electroencephalogram (MI-EEG) based brain-computer interface (BCI) is a
burgeoning auxiliary means to realize rehabilitation therapy. One of the major concerns in …
burgeoning auxiliary means to realize rehabilitation therapy. One of the major concerns in …
MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network
S Tiwari, S Goel, A Bhardwaj - Applied Intelligence, 2022 - Springer
Abstract In recent times, Motor Imagery (MI) tasks have gained great attraction among
researchers in the field of Brain-Computer Interface (BCI). The MI tasks are the core field of …
researchers in the field of Brain-Computer Interface (BCI). The MI tasks are the core field of …
Ensemble regularized common spatio-spectral pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification
MN Cherloo, HK Amiri, MR Daliri - Computers in biology and medicine, 2021 - Elsevier
Abstract The Brain-Computer interface system provides a communication path among the
brain and computer, and recently, it is the subject of increasing attention. One of the most …
brain and computer, and recently, it is the subject of increasing attention. One of the most …
Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs
In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …
Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the …
One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram
Cognitive/mental task classification using single/limited channel (s) electroencephalogram
(EEG) signals in real-time play an important role in designing portable brain-computer …
(EEG) signals in real-time play an important role in designing portable brain-computer …
Boosting motor imagery brain-computer interface classification using multiband and hybrid feature extraction
Brain-computer interface (BCI) is a new promising technology for control and
communication, the BCI system aims to decode the measured brain activity into a command …
communication, the BCI system aims to decode the measured brain activity into a command …
A machine learning approach for walking classification in elderly people with gait disorders
Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which
restricts their mobility independence. The physical health and well-being of the elderly …
restricts their mobility independence. The physical health and well-being of the elderly …