MultiSDGAN: Translation of OCT Images to Superresolved Segmentation Labels Using Multi-Discriminators in Multi-Stages.

Optical coherence tomography (OCT) has been identified as a non-invasive and inexpensive imaging modality to discover potential biomarkers for Alzheimer's diagnosis and progress determination. Current hypotheses presume the thickness of the retinal layers, which are analyzable within OCT scans, as an effective biomarker for the presence of Alzheimer's. As a logical first step, this work concentrates on the accurate segmentation of retinal layers to isolate the layers for further analysis.

Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification.

A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear.

Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses.

One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident.

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective.

Time-varying and simultaneous frequency stimulation for multi-class SSVEP-based brain-computer interface.

In recent years, there has been increasing interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems for their high signal to noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are limited. The goal of this paper is to investigate time-varying and simultaneous frequency stimulation in order to increase the number of visual stimuli with a fixed number of stimulation frequencies in multiclass SSVEP-based BCI systems.

A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies.

Steady-state visual evoked potential (SSVEP) has become one of the most widely employed modalities in online brain computer interface (BCI) because of its high signal-to-noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are generally limited for practical applications. In this paper, we introduce a novel stimulation method using patterns of time-varying frequencies that can increase the number of visual stimuli with a fixed number of stimulation frequencies for use in multi-class SSVEP-based BCI systems.

The Impact of Vibrotactile Biofeedback on the Excessive Walking Sway and the Postural Control in Elderly.

Gait and postural control are important aspects of human movement and balance. Normal movement control in human is subject to change with aging. With aging, the nervous system comprising, somatosensory, visual senses, spatial orientation senses, and neuromuscular control degrade.

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration.