Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review
Background Rehabilitation medicine is facing a new development phase thanks to a recent
wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This …
wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
Deep convolution generative adversarial network-based electroencephalogram data augmentation for post-stroke rehabilitation with motor imagery
The motor imagery brain–computer interface (MI-BCI) system is currently one of the most
advanced rehabilitation technologies, and it can be used to restore the motor function of …
advanced rehabilitation technologies, and it can be used to restore the motor function of …
Cross-validation of predictive models for functional recovery after post-stroke rehabilitation
Background Rehabilitation treatments and services are essential for the recovery of post-
stroke patients' functions; however, the increasing number of available therapies and the …
stroke patients' functions; however, the increasing number of available therapies and the …
Upper limb movement classification via electromyographic signals and an enhanced probabilistic network
Few studies in the literature have researched the use of surface electromyography (sEMG)
for motor assessment post-stroke due to the complexity of this type of signal. However …
for motor assessment post-stroke due to the complexity of this type of signal. However …
Predicting outcome in patients with brain injury: differences between machine learning versus conventional statistics
Defining reliable tools for early prediction of outcome is the main target for physicians to
guide care decisions in patients with brain injury. The application of machine learning (ML) …
guide care decisions in patients with brain injury. The application of machine learning (ML) …
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-
based models can produce feedback from physiological data such as …
based models can produce feedback from physiological data such as …
Feature selection combining filter and wrapper methods for motor-imagery based brain–computer interfaces
Motor imagery (MI) based brain–computer interfaces help patients with movement disorders
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
to regain the ability to control external devices. Common spatial pattern (CSP) is a popular …
Optimization of model training based on iterative minimum covariance determinant in motor-imagery BCI
The common spatial patterns (CSP) algorithm is one of the most frequently used and
effective spatial filtering methods for extracting relevant features for use in motor imagery …
effective spatial filtering methods for extracting relevant features for use in motor imagery …
Concurrent prediction of finger forces based on source separation and classification of neuron discharge information
A reliable neural-machine interface is essential for humans to intuitively interact with
advanced robotic hands in an unconstrained environment. Existing neural decoding …
advanced robotic hands in an unconstrained environment. Existing neural decoding …