Neurology

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In compar...

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in dif...

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learni...

Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot.

Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation p...

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template.

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This s...

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is li...

Therapeutic use of the humanoid robot, Telenoid, with older adults: A critical interpretive synthesis review.

This review sought to critically evaluate the use of the teleoperated humanoid robotic communications device, Telenoid, for therapeutic purposes with ...

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal.

Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities recorded via Electroencephalogram (EEG) signals is a p...

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address t...

Machine Learning for Detection of Muscular Activity from Surface EMG Signals.

BACKGROUND: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not ava...

Stress Classification Using Brain Signals Based on LSTM Network.

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals ar...

Single Neuron for Solving XOR like Nonlinear Problems.

XOR is a special nonlinear problem in artificial intelligence (AI) that resembles multiple real-world nonlinear data distributions. A multiplicative n...

EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot.

Upper-limb rehabilitation therapy sessions for post-stroke people generally contain rhythmic hand movements in a tiresome manner to rebuild the injure...

Robot touch with speech boosts positive emotions.

A gentle touch is an essential part of human interaction that produces a positive care effect. Previously, robotics studies have shown that robots can...

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning.

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke d...

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict o...

Editorial Comment to Impact of nerve sparing in robot-assisted radical prostatectomy on the risk of positive surgical margin and biochemical recurrence.

Dietary administration of D-chiro-inositol attenuates sex-specific metabolic imbalances in the 5xFAD mouse model of Alzheimer's disease.

Increasing evidence shows that hypothalamic dysfunction, insulin resistance, and weight loss precede and progress along with the cognitive decline in ...

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was prop...

Dementia risk predictions from German claims data using methods of machine learning.

INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regres...