Background For non-invasive brain-computer interface systems (BCIs) with multiple
electroencephalogram (EEG) channels, the key factor limiting their convenient application in …

Background Channel selection has become the pivotal issue affecting the widespread
application of non-invasive brain-computer interface systems in the real world. However …

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-
signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in …

Brain-computer interface systems and the recording of brain activity has garnered significant
attention across a diverse spectrum of applications. EEG signals have emerged as a …

As different region of the brain is associated with different mental activity, channel selection
is commonly used to enhance the performance of multi-electrode electroencephalography …

Due to the development of convenient brain–machine interfaces (BMIs), the automatic
selection of a minimum channel (electrode) set has attracted increasing interest because the …

Abstract Multichannel Electroencephalography-based Brain-Computer Interface (BCI)
systems facilitate a communicating medium between the human brain and the outside world …

Feature selection is crucial to develop a brain computer interface (BCI) system which has
high classification accuracy and less computational complexity in especially a large feature …

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves
high-dimensional features, which increases the computational complexity. To reduce this …

Objective. Multi-channel electroencephalogram data containing redundant information and
noise may result in low classification accuracy and high computational complexity, which …