Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
Smart wireless health care system using graph LSTM pollution prediction and dragonfly node localization
Wireless sensing networks (WSNs) have been applied on various research applications
such as monitoring health of humans, targets tracking, natural resources investigation, air …
such as monitoring health of humans, targets tracking, natural resources investigation, air …
Biot: Biosignal transformer for cross-data learning in the wild
Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …
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 …
Recent approaches on classification and feature extraction of EEG signal: A review
Objective: Electroencephalography (EEG) has an influential role in neuroscience and
commercial applications. Most of the tools available for EEG signal analysis use machine …
commercial applications. Most of the tools available for EEG signal analysis use machine …
[HTML][HTML] Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface
R Dhiman - Measurement: Sensors, 2023 - Elsevier
Abstract Decryption of Motor Imagery (MI) activity from an Electroencephalogram (EEG) data
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
is a significant part of the Brain-Computer Interface (BCI) technology that allows motor …
Recognition of the mental workloads of pilots in the cockpit using EEG signals
The commercial flightdeck is a naturally multi-tasking work environment, one in which
interruptions are frequent come in various forms, contributing in many cases to aviation …
interruptions are frequent come in various forms, contributing in many cases to aviation …
RETRACTED ARTICLE: Automated query classification based web service similarity technique using machine learning
With the tremendous growth of the internet, services provided through the internet are
increasing day by day. For the adaption of web service techniques, several standards like …
increasing day by day. For the adaption of web service techniques, several standards like …
Recent trends in EEG based Motor Imagery Signal Analysis and Recognition: A comprehensive review.
The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …