Assessing Fit of Nontraditional Assistive Technologies

Abstract

There is a variety of brain-based interface methods which depend on measuring small changes in brain signals or properties. These methods have typically been used for nontraditional assistive technology applications. Non-traditional assistive technology is generally targeted for users with severe motor disabilities which may last long-term due to illness or injury or short-term due to situational disabilities. Control of a nontraditional assistive technology can vary widely across users depending upon many factors ranging from health to experience. Unfortunately, there is no systematic method for assessing usability of nontraditional assistive technologies to achieve the best control. The current methods to accommodate users through trial-and-error result in the loss of valuable time and resources as users sometimes have diminishing abilities or suffer from terminal illnesses. This work describes a methodology for objectively measuring an individual's ability to control a specific nontraditional assistive technology, thus expediting the technology-fit process.

Fuzzy Logic Based Eye-Brain Controlled Web Access System

“…(Kübler et al, 2009; see also Randolph & Moore Jackson, 2010). The data in the sample provides sufficient evidence to reject the null hypothesis with an alpha of 0.01 (p-value < 0.001).…”

“…The experiment investigated how accurately a cross-section of individuals could navigate a webpage using this system. Following previous researchers investigating the performance of non-traditional assistive technologies (for example, Randolph & Moore Jackson, 2010), this research studied individuals' initial use of FLYAWAY with just a short introduction. Training effects or learning through experience due to long-term use of the system were not incorporated in this study.…”

“…EEG is the most common approach for brain signal acquisition without surgically implanted devices (Randolph & Moore Jackson, 2010). The EEG control augments the eye tracking interface to serve as another level of control.…”

Ordinal Pattern Analysis Method Applied in a P300-based Brain Computer Interface

“…Therefore, it is an excellent choice for people who are suffering from motor disabilities caused by any disease impairs the neural pathways that control muscles, or impairs the muscles themselves [10]. Furthermore, it provides a communication option for nondisabled users with situational disabilities induced by their environment, such as with aircraft pilots and astronauts subjected to extreme forces, soldiers in hostile territory, and even video gamers [11].…”

Individual Characteristics and Their Effect on Predicting Mu Rhythm Modulation

“…Unfortunately this approach can waste valuable time and resources as users sometimes have diminishing abilities or suffer from terminal illnesses. There have been efforts to characterize the degree of controllability of a BCI by an individual Randolph, Moore Jackson, Mason, & McCampbell, 2005), but ties between controllability and an individual's characteristics are just beginning to be investigated (Allison et al, 2010;Guger et al, 2009;Kübler & Birbaumer, 2008;Randolph & Moore Jackson, 2010). Individual characteristics are the distinguishing factors between people and include their demographic, physiological, and cognitive differences.…”