A Survey of EEG and Machine Learning based methods for Neural Rehabilitation
One approach to therapy and training for the restoration of damaged muscles and motor
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
Toward the development of versatile brain–computer interfaces
Recent advances in artificial intelligence demand an automated framework for the
development of versatile brain–computer interface (BCI) systems. In this article, we …
development of versatile brain–computer interface (BCI) systems. In this article, we …
A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment
K Kyriaki, D Koukopoulos, CA Fidas - IEEE Access, 2024 - ieeexplore.ieee.org
Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive
Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly …
Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly …
Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain
Electroencephalography (EEG) signals are considered one of the oldest techniques for
detecting disorders in medical signal processing. However, brain complexity and the non …
detecting disorders in medical signal processing. However, brain complexity and the non …
Computerized multidomain EEG classification system: A new paradigm
The recent advancements in electroencepha-logram (EEG) signals classification largely
center around the domain-specific solutions that hinder the algorithm cross-discipline …
center around the domain-specific solutions that hinder the algorithm cross-discipline …
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 …
[HTML][HTML] Single-trial stimuli classification from detected P300 for augmented Brain–Computer Interface: A deep learning approach
The purpose of advanced Brain–Computer Interfaces (BCIs) is to connect the human brain
with an external device without using the muscular system. To do this, they must effectively …
with an external device without using the muscular system. To do this, they must effectively …
Tensor factorization and attention-based CNN-LSTM deep-learning architecture for improved classification of missing physiological sensors data
M Akmal - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
One of the essential issues for efficient control of prosthesis is the accurate classification of
target movements hidden in electroencephalography (EEG) and electromyography (EMG) …
target movements hidden in electroencephalography (EEG) and electromyography (EMG) …
CIS feature selection based dynamic ensemble selection model for human stress detection from EEG signals
Stress has an impact not only on a person's physical health but also on his or her ability to
perform at work, passion, and attitude in day-to-day life. It is one of the most difficult …
perform at work, passion, and attitude in day-to-day life. It is one of the most difficult …