Motor imagery (MI) responses extracted from the brain in the form of EEG signals have been widely utilized for intention detection in brain computer interface (BCI) systems. However, due to the non-linearity and the nonstationarity of EEG signals, BCI systems suffer from low MI prediction rate with both known and unknown influncing factors. This paper investigates the impact of visual stimulus, feature dimensions and artifacts on MI task detection rate, towards improving MI prediction rate. Three EEG datasets were utilized to facilitate the investigation. Three filters (band-pass, notch and common average reference) and the independent component analysis (ICA) were applied on each datasets, to eliminate the impact of artifact. Three sets of...
Many of the algorithms for brain computer interface development are both complex and specific to a p...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
Abstract — The implementation of a realistic Brain-Computer Interface (BCI) for non-trained subjects...
Brain computer interface (BCI) is known as a good way to communicate between brain and computer or o...
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity ...
International audienceMotor Imagery-based Brain-Computer Interfaces (MI-BCI) allow users to control ...
Independent component analysis (ICA) as a promising spatial filtering method can separate motor-rela...
Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulat...
In recent years, the Brain-Computer Interface (BCI), has been a very popular topic globally. BCI is...
Artefacts in recordings of the electroencephalogram (EEG) are a common problem in Brain-Computer Int...
The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied techno...
Motor imagery (MI) tasks classification provides an important basis for designing brain computer int...
Electroencephalogram (EEG) based brain-computer interfaces (BCIs) enable communication by interpreti...
Abstract—Motor imagery electroencephalography (EEG), which embodies cortical potentials during menta...
Brain-computer interface (BCI) technology can return the ability to communicate to those suffering f...
Many of the algorithms for brain computer interface development are both complex and specific to a p...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
Abstract — The implementation of a realistic Brain-Computer Interface (BCI) for non-trained subjects...
Brain computer interface (BCI) is known as a good way to communicate between brain and computer or o...
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity ...
International audienceMotor Imagery-based Brain-Computer Interfaces (MI-BCI) allow users to control ...
Independent component analysis (ICA) as a promising spatial filtering method can separate motor-rela...
Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulat...
In recent years, the Brain-Computer Interface (BCI), has been a very popular topic globally. BCI is...
Artefacts in recordings of the electroencephalogram (EEG) are a common problem in Brain-Computer Int...
The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied techno...
Motor imagery (MI) tasks classification provides an important basis for designing brain computer int...
Electroencephalogram (EEG) based brain-computer interfaces (BCIs) enable communication by interpreti...
Abstract—Motor imagery electroencephalography (EEG), which embodies cortical potentials during menta...
Brain-computer interface (BCI) technology can return the ability to communicate to those suffering f...
Many of the algorithms for brain computer interface development are both complex and specific to a p...
Brain-computer interface (BCI) is a promising technique which analyses and translates brain signals ...
Abstract — The implementation of a realistic Brain-Computer Interface (BCI) for non-trained subjects...