A5023970363- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Gaze Tracking and Assistive Technology
- Medical Image Segmentation Techniques
- AI in cancer detection
- Neural Networks and Applications
- Neural and Behavioral Psychology Studies
- Tactile and Sensory Interactions
- Non-Invasive Vital Sign Monitoring
- Neural dynamics and brain function
- Cognitive Science and Mapping
- Advanced Vision and Imaging
- Obstructive Sleep Apnea Research
- Neuroscience and Music Perception
- Indoor and Outdoor Localization Technologies
- Advanced Memory and Neural Computing
- Cell Image Analysis Techniques
FORTH Institute of Computer Science
2022
FORTH Institute of Electronic Structure and Laser
2019-2022
Foundation for Research and Technology Hellas
2010-2019
University of Crete
2010
Field Dependence-Independence (FDI) is a widely studied dimension of cognitive styles designed to measure an individual's ability identify embedded parts organized visual field as entities separate from that given field. The research aims determine whether the brain activity features are considered be perceptual switching indicators could serve robust features, differentiating Field-Dependent (FD) Field-Independent (FI) participants. Previous suggests various derived event related potentials...
Brain computer interfaces (BCIs) that are focused on navigation applications have been developed for patients suffering from severe paralysis to offer a means of autonomy. In SSVEP-based BCIs, users focus their gaze flickering targets, which correspond specific commands. Besides the accuracy target identification, several additional aspects important development practical and useful BCI, such as low cost, ease use robustness everyday-life conditions. previous paper, we presented an BCI...
Vital sign monitoring is essential in evaluating a person's health status and disease progress. However, most commonly used contact-based techniques may have drawbacks, such as loss of contact skin irritations that often turn them inappropriate for long term remote continuous monitoring. Contactless approaches on the other hand offer great ease use, are non-disturbing, be more suitable everyday usage long-term By exploiting advances machine learning, they comparable accuracy to...
Electroencephalography-based brain computer interfaces (BCIs) have been proposed as a promising non-invasive approach for people suffering from neuromuscular disorders to facilitate control and communication with the surrounding environment. Specifically, steady-state visual evoked potentials (SSVEPs) successfully used in navigation applications mainly due their efficiency quick response time. In this work we present an SSVEP-based BCI remote robotic car navigation. The robot telepresence is...
A promising application of Brain Computer Interfaces (BCIs), and in particular Steady-State Visually Evoked Potentials (SSVEP) is wheelchair navigation which can facilitate the daily life patients suffering from severe paralysis. However, outdoor performance such a system highly affected by uncontrolled environmental factors. In this paper, we present an SSVEP-based propose incremental learning as method adapting to changing conditions.
Brain tomographic techniques, such as MRI provide a plethora of pathophysiological tissue information that assists the clinician in diagnosis, therapy design/monitoring and surgery. Robust segmentation brain tissues is very important task order to perform number computational tasks including morphological measurements structures, automatic detection asymmetries pathologies, simulation growth. In this paper we present structure results based on our implementation mean-shift algorithm compare...
Electroencephalography-based brain computer interfaces (BCIs) have been widely used in assistive applications for patients suffering from quadriplegia, or even the locked-in syndrome, to promote autonomy and control. Steady-state visual evoked potentials (SSVEPs) is a BCI stimulation protocol that has employed navigation applications, due their efficiency fast response time. In current study, we use previously developed SSVEP-based robotic car with low-cost EEG recording device, compare...