Tagged: Communication system control

Electrooculogram based system for computer control using a multiple feature classification model

This paper discusses the creation of a system for computer-aided communication through automated analysis and processing of electrooculogram signals. In situations of disease or trauma, there may be an inability to communicate with others through standard means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrig’s disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, the Telepathix system was designed to accept eye movement commands denoted by looking to the left, looking to the right, and looking straight ahead to navigate a virtual keyboard. Using a ternary virtual keyboard layout and a multiple feature classification model, a typing speed of 6 letters per minute was achieved

The Bilinear Brain: Towards Subject‐Invariant Analysis

A major challenge in single-trial electroencephalography (EEG) analysis and Brain Computer Interfacing (BCI) is the so called, inter-subject/inter-session variability: (i.e large variability in measurements obtained during different recording sessions). This variability restricts the number of samples available for single-trial analysis to a limited number that can be obtained during a single session. Here we propose a novel method that distinguishes between subject-invariant features and subject-specific features, based on a bilinear formulation. The method allows for one to combine multiple recording of EEG to estimate the subject-invariant parameters, hence addressing the issue of inter-subject variability, while reducing the complexity of estimation for the subject-specific parameters. The method is demonstrated on 34 datasets from two different experimental paradigms: Perception categorization task and Rapid Serial Visual Presentation (RSVP) task. We show significant improvements in classification performance over state-of-the-art methods. Further, our method extracts neurological components never before reported on the RSVP thus demonstrating the ability of our method to extract novel neural signatures from the data.