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 …
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 …
An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals
Abstract Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide
insight into consumers responses on marketing stimuli. In order to achieve insight …
insight into consumers responses on marketing stimuli. In order to achieve insight …
BCI-based consumers' choice prediction from EEG signals: an intelligent neuromarketing framework
Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
customers react to marketing stimuli. Marketers spend about $750 billion annually on …
A deep learning approach for early wildfire detection from hyperspectral satellite images
Wildfires are getting more severe and destructive. Due to their fast-spreading nature,
wildfires are often detected when already beyond control and consequently cause billion …
wildfires are often detected when already beyond control and consequently cause billion …
Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach
Brain–computer interface (BCI) is an important alternative for disabled people that enables
the innovative communication pathway among individual thoughts and different assistive …
the innovative communication pathway among individual thoughts and different assistive …
Review of EEG Signals Classification Using Machine Learning and Deep-Learning Techniques
F Hassan, SF Hussain - Advances in Non-Invasive Biomedical Signal …, 2023 - Springer
Electroencephalography (EEG) signals have been widely used for the prognosis and
diagnosis of several disorders, such as epilepsy, schizophrenia, Parkinson's disease etc …
diagnosis of several disorders, such as epilepsy, schizophrenia, Parkinson's disease etc …
Remote sensing meets deep learning: exploiting spatio-temporal-spectral satellite images for early wildfire detection
TC Phan, TT Nguyen - 2019 - infoscience.epfl.ch
Wildfires are getting more severe and destructive. Due to their fast-spreading nature,
wildfires are often detected when already beyond control and consequently cause billion …
wildfires are often detected when already beyond control and consequently cause billion …
An Automatic Approach to Control Wheelchair Movement for Rehabilitation Using Electroencephalogram
Modern science and technology development enables humans to control devices using their
brains. brain computer interface (BCI) system using EEG (Electroencephalogram) is a non …
brains. brain computer interface (BCI) system using EEG (Electroencephalogram) is a non …
Wink based facial expression classification using machine learning approach
Facial expression may establish communication between physically disabled people and
assistive devices. Different types of facial expression including eye wink, smile, eye blink …
assistive devices. Different types of facial expression including eye wink, smile, eye blink …