Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
[HTML][HTML] Brain computer interfaces, a review
A brain-computer interface (BCI) is a hardware and software communications system that
permits cerebral activity alone to control computers or external devices. The immediate goal …
permits cerebral activity alone to control computers or external devices. The immediate goal …
Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface
AM Roy - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Abstract Objective. Deep learning (DL)-based brain–computer interface (BCI) in motor
imagery (MI) has emerged as a powerful method for establishing direct communication …
imagery (MI) has emerged as a powerful method for establishing direct communication …
EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …
Optimizing spatial filters for robust EEG single-trial analysis
Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a
rather blurred image of brain activity. Therefore spatial filters are extremely useful in single …
rather blurred image of brain activity. Therefore spatial filters are extremely useful in single …
Brain–machine interfaces: past, present and future
Since the original demonstration that electrical activity generated by ensembles of cortical
neurons can be employed directly to control a robotic manipulator, research on brain …
neurons can be employed directly to control a robotic manipulator, research on brain …
A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
Multiclass brain–computer interface classification by Riemannian geometry
This paper presents a new classification framework for brain-computer interface (BCI) based
on motor imagery. This framework involves the concept of Riemannian geometry in the …
on motor imagery. This framework involves the concept of Riemannian geometry in the …
An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces
AM Roy - Biomedical Signal Processing and Control, 2022 - Elsevier
Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …
Brain oscillations and the importance of waveform shape
Oscillations are a prevalent feature of brain recordings. They are believed to play key roles
in neural communication and computation. Current analysis methods for studying neural …
in neural communication and computation. Current analysis methods for studying neural …