Neurology

[Use AI for Dementia Diagnosis].

Using AI for dementia diagnosis is still in research stage, however some of the work points to the development of new medical devices. One is a voxel-based morphometry analysis of brain atrophy, and a brain network analysis using a resting state functional MRI and diffusion tensor imaging. The other is an application to detect dementia in daily life using "IoT" technology. As it has been determine...

[Support for Patients and Their Family Using Artificial Intelligence].

For patients with dementia and their family, artificial intelligence (AI) has been utilized to support diagnosis and evaluation. In addition, communication robots equipped with AI offer a way to maintain the cognitive functions of patients with dementia. Moreover, AI is currently used for fall prevention and to define the needs of patients and their family and to convey them to develop new devices...

Characterization of SSMVEP-based EEG signals using multiplex limited penetrable horizontal visibility graph.

The steady state motion visual evoked potential (SSMVEP)-based brain computer interface (BCI), which incorporates the motion perception capabilities o...

Estimation of brain connectivity through Artificial Neural Networks.

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved...

Signal2Image Modules in Deep Neural Networks for EEG Classification.

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such a...

EEG-Based Emotion Recognition with Similarity Learning Network.

Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evalua...

Classification of Perceived Human Stress using Physiological Signals.

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These inclu...

Deep Learning Approach for Highly Specific Atrial Fibrillation and Flutter Detection based on RR Intervals.

Atrial fibrillation (AF) and atrial flutter (AFL) represent atrial arrhythmias closely related to increasing risk for embolic stroke, and therefore be...

2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep.

We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable progn...

Deep Learning Techniques for Improving Digital Gait Segmentation.

Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously l...

Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks.

We describe and assess convolutional neural network (CNN) models for detection of glaucoma based upon optical coherence tomography (OCT) retinal nerve...

Facial Recognition Task for the Classification of Mild Cognitive Impairment with Ensemble Sparse Classifier.

Conventional methods for detecting mild cognitive impairment (MCI) require cognitive exams and follow-up neuroimaging, which can be time-consuming and...

Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction.

Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal ...

Epileptic States Recognition Using Transfer Learning.

Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy...

A convolutional neural network based framework for classification of seizure types.

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures ba...

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an important research direction. A novel automatic epilep...

Visualized Evidences for Detecting Novelty in Myoelectric Pattern Recognition using 3D Convolutional Neural Networks.

Although myoelectric pattern recognition (MPR) has been considered as a milestone technique to enable dexterous control of multiple degrees of freedom...

Alzheimer's Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation.

Alzheimer's disease significantly affects the quality of life of patients. This paper proposes an approach to identify Alzheimer's disease based on tr...

Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.

Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully r...

Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks.

The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton reh...