Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
N Tibrewal, N Leeuwis, M Alimardani - Plos one, 2022 - journals.plos.org
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain
activity patterns associated with mental imagination of movement and convert them into …
activity patterns associated with mental imagination of movement and convert them into …
An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information
Background Common spatial pattern (CSP) has been an effective technique for feature
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
[HTML][HTML] Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …
Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface
A common assumption in traditional supervised learning is the similar probability distribution
of data between the training phase and the testing/operating phase. When transitioning from …
of data between the training phase and the testing/operating phase. When transitioning from …
Active physical practice followed by mental practice using BCI-driven hand exoskeleton: a pilot trial for clinical effectiveness and usability
Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke
rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we …
rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we …
Motor imagery EEG spectral-spatial feature optimization using dual-tree complex wavelet and neighbourhood component analysis
Background Frequency band optimization improves the performance of common spatial
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
A novel channel selection method for BCI classification using dynamic channel relevance
A Tiwari, A Chaturvedi - IEEE Access, 2021 - ieeexplore.ieee.org
Brain-Computer Interface (BCI) provides a direct communicating pathway between the
human brain and the external environment. In the BCI systems, electroencephalography …
human brain and the external environment. In the BCI systems, electroencephalography …
Single-trial EEG classification with EEGNet and neural structured learning for improving BCI performance
Research and development of new machine learning techniques to augment the
performance of Brain-computer Interfaces (BCI) have always been an open area of interest …
performance of Brain-computer Interfaces (BCI) have always been an open area of interest …
Three-class EEG-based motor imagery classification using phase-space reconstruction technique
Over the last few decades, brain signals have been significantly exploited for brain-computer
interface (BCI) applications. In this paper, we study the extraction of features using event …
interface (BCI) applications. In this paper, we study the extraction of features using event …
Deep learning based prediction of EEG motor imagery of stroke patients' for neuro-rehabilitation application
H Raza, A Chowdhury… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …