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 review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to
control external devices. This paper presents the decoding of intuitive upper extremity …
control external devices. This paper presents the decoding of intuitive upper extremity …
Unsupervised feature extraction with autoencoders for EEG based multiclass motor imagery BCI
Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important
component of BCI system that helps motor-disabled people interact with the outside world …
component of BCI system that helps motor-disabled people interact with the outside world …
[HTML][HTML] Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction
Brain–computer interfaces are used for direct two-way communication between the human
brain and the computer. Brain signals contain valuable information about the mental state …
brain and the computer. Brain signals contain valuable information about the mental state …
Recognition of EEG signal motor imagery intention based on deep multi-view feature learning
J Xu, H Zheng, J Wang, D Li, X Fang - Sensors, 2020 - mdpi.com
Recognition of motor imagery intention is one of the hot current research focuses of brain-
computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their …
computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their …
Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …
Motor-imagery-based brain–computer interface using signal derivation and aggregation functions
Brain–computer interface (BCI) technologies are popular methods of communication
between the human brain and external devices. One of the most popular approaches to BCI …
between the human brain and external devices. One of the most popular approaches to BCI …
Feature extraction and identification of Alzheimer's disease based on latent factor of multi-channel EEG
K Li, J Wang, S Li, H Yu, L Zhu, J Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Alzheimer's disease is a neurodegenerative disease in old age, early diagnosis will help to
delay the progression of the disease. Presently, the features of brain functional diseases can …
delay the progression of the disease. Presently, the features of brain functional diseases can …
Automated arrhythmia detection based on RR intervals
Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL
are examples of arrhythmia that affect a growing number of patients. This paper describes a …
are examples of arrhythmia that affect a growing number of patients. This paper describes a …