Convolutional Models for Segmentation and Localization


The goal of new researches and technologies is to help and ease everyday tasks in life. Today, we can find the assistive technologies in everyday use, from the voice commands in smartphones to the eye tracking technologies. Those technologies emerged from the human-computer interface field of research. Brain-computer interfaces (BCIs) are part of this wide field of research. The main goal of such systems is to connect human (subjects) intention and the physical interaction with environment to do some task. In such a way, difficulties with interaction for people who are unable to use current devices (disabled persons) or are completely unable to communicate with outside world (patients with locked-in syndrome) are bypassed. The applications of the BCI systems can be of great help if they are developed for the people with severe neuromuscular damage, occurred as the effect of spinal cord injury, amyotrophic lateral sclerosis, stroke, or cerebral paralysis [1]. Besidea these specific applications, the BCI systems can be used as part of the biofeedback systems, to record our psychophysical state (perhaps even unaware of it) and use the computer to train us or adapt the environment to suit our needs. the smart house, wheelchair control) to complex ones like robot or artificial prosthesis control. On Fig. 1 general scheme of the BCI system is shown.

In HATZ 2018.