Neurology

Machine Learning Approach for Music Familiarity Classification with Single-Channel EEG.

Recognition of familiar music on brainwaves through machine learning (ML) can be instrumental in innovative therapeutic devices that improve memory and communication in dementia patients. In this study, a variety of machine learning algorithms were applied, including Random Forest (RF), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Deep Learning (D...

Unsupervised Gait Assessments of Stroke Patients Using a Smartphone and Machine Learning.

Home-based rehabilitation is a trend of post-stroke lower limb rehabilitation, aimed at a long-term and higher dose of therapy. Unsupervised gait assessments can help therapists to track patients' recovery progress and timely adjust rehabilitation interventions. This study aims to develop a smartphone-based wireless system for unsupervised gait assessments of stroke patients. The proposed system i...

Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework.

Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-c...

Therapy for Abnormal Muscle Synergies in Stroke Using the ULIX Low-Impedance Robot.

Patients who suffer from stroke often experience synergistic movements that make completion of activities of daily living (ADL) difficult. Robotics is...

Enhancing sleep stage classification with 2-class stratification and permutation-based channel selection.

We present a method that uses a convolutional neural network (CNN) called EEGNeX to extract and classify the characteristics of sleep-related waveform...

Automated Intraoperative Visual Detection of Pediatric Epileptogenic Brain Lesions Using a Machine Learning Classifier.

450,000 children with epilepsy in the United States suffer lifelong disability and are at risk of sudden death. Surgical treatment of epilepsy is limi...

Dysarthria Detection with Deep Representation Learning for Patients with Parkinson's Disease.

Dysarthria is a very common motor speech symptom in Parkinson's disease impairing normal communications of patients. Detection of dysarthria could ass...

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnost...

Resource-Efficient Continual Learning for Personalized Online Seizure Detection.

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant c...

Deep Residual Neural Networks for Spatial EEG Source Imaging.

EEG source imaging is an indispensable tool for non-invasive study of brain function. Existing methods mainly directly deal with the EEG inverse probl...

A Regression Framework for Predicting Cognitive Decline in Frontotemporal Dementia using Recurrent Neural Networks.

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder with a diverse range of symptoms, including personality changes, behavioral ...

EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals c...

Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers.

Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focus...

Estimating Upper-extremity Function with Raw Kinematic Trajectory Data after Stroke using End-to-end Machine Learning Approach.

Although there are some studies on the automatic evaluation of impairment levels after stroke using machine learning (ML) models, few have delved into...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are dev...

Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automate...

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics.

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like ele...

Machine Learning Exploration of Brain Morphological Features and Sensory Measures.

Previous investigations have implicated the neuroanatomical basis of sensory systems; however, definitive neuroimaging biomarkers remain elusive. The ...

Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remo...

HRV-based Monitoring of Neonatal Seizures with Machine Learning.

With the rapid development of machine learning (ML) in biomedical signal processing, ML-based neonatal seizure detection using heart rate variability ...