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 online classification performance in motor imagery-based brain–computer interfaces for stroke neurorehabilitation
Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential
for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice …
for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice …
Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification
KR Balmuri, SR Madala, PB Divakarachari… - Asian Journal of …, 2023 - Wiley Online Library
Abstract In Brain Computer Interface (BCI), achieving a reliable motor‐imagery classification
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
is a challenging task. The set of discriminative and relevant feature vectors plays a crucial …
Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification
Analyzing electroencephalography (EEG) signals with machine learning approaches has
become an attractive research domain for linking the brain to the outside world to establish …
become an attractive research domain for linking the brain to the outside world to establish …
Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis
Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
Enhancing EEG-based mental stress state recognition using an improved hybrid feature selection algorithm
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition
require a conventional wearable device. This, in turn, requires an efficient number of EEG …
require a conventional wearable device. This, in turn, requires an efficient number of EEG …
Joint spatial and temporal features extraction for multi-classification of motor imagery EEG
X Jia, Y Song, L Yang, L **e - Biomedical Signal Processing and Control, 2022 - Elsevier
The application of brain-computer interface (BCI) has always been limited by low decoding
accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional …
accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional …
K-means pelican optimization algorithm based search space reduction for remote sensing image retrieval
In remote sensing field, the image retrieval is considered a complex task and attained higher
attention, because of the data acquired from the earth observation satellites. An …
attention, because of the data acquired from the earth observation satellites. An …
Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineering
Feature selection aims to explore the characteristics of a problem that is under investigation
instead of focusing on extracting (deep) features or classification tasks. The pending issues …
instead of focusing on extracting (deep) features or classification tasks. The pending issues …
Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer …
The accuracy of classifying motor imagery (MI) activities is a significant challenge when
using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control …
using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control …