Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.

Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated.

SSVEP based Wheelchair Navigation in Outdoor Environments.

A promising application of Brain Computer Interfaces (BCIs), and in particular of Steady-State Visually Evoked Potentials (SSVEP) is wheelchair navigation which can facilitate the daily life of patients suffering from severe paralysis. However, the outdoor performance of such a system is highly affected by uncontrolled environmental factors. In this paper, we present an SSVEP-based wheelchair navigation system and propose incremental learning as a method of adapting the system to changing environmental conditions.

Assessing Field Dependence-Independence Cognitive Abilities Through EEG-Based Bistable Perception Processing.

Field Dependence-Independence (FDI) is a widely studied dimension of cognitive styles designed to measure an individual's ability to identify embedded parts of an organized visual field as entities separate from that given field. The research aims to determine whether the brain activity features that are considered to be perceptual switching indicators could serve as robust features, differentiating Field-Dependent (FD) from Field-Independent (FI) participants. Previous research suggests that various features derived from event related potentials (ERP) and frequency features are associated with the perceptual reversal occurring during the observation of a bistable image.

Applicability of SSVEP-based brain-computer interfaces for robot navigation in real environments.

Brain-computer interfaces have been extensively studied and used in order to aid patients suffering from neuromuscular diseases to communicate and control the surrounding environment. Steady-state visual evoked potentials (SSVEP) constitute a very popular BCI stimulation protocol, due to their efficiency and quick response time. In this study, we developed a SSVEP-based BCI along with a low-cost custom radio-controlled robot-car providing live video feedback from a wireless camera mounted on the robot, serving as our testbed.

Absence seizure epilepsy detection using linear and nonlinear EEG analysis methods.

In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.