P300-Based Asynchronous Brain Computer Interface for Environmental Control System.

An asynchronous brain computer interface (A-BCI) determines whether or not a subject is on control state, and produces control commands only in case of subject's being on control state. In this study, we propose a novel P300-based A-BCI algorithm that distinguishes control state and noncontrol state of users. Furthermore, A-BCI algorithm combined with a dynamic stopping function that enables users to select control command independent from a fixed number of intensification sequence. The proposed P300-based A-BCI algorithm uses classification patterns to determine control state and uses optimal operating point of receiver operating characteristics curve for dynamic stopping function. The proposed A-BCI algorithm is also combined with region-based paradigm (RBP) based stimulus interface. The A-BCI algorithm is tested on an internet-based environmental control system. A total of ten nondisabled subjects were participated voluntarily in the experiments. Two-level approach of the RBP-based stimulus interface improves noncontrol state detection accuracy up to 100%. Besides, ratio of incorrect command selection at control state is reduced significantly. At control state, ratio of correct selections, incorrect selections, and missed selections are 93.27%, 1.09%, and 5.63%, respectively. On the other hand, dynamic stopping function enables command selections at least two intensifications. Mean number of intensification sequences to complete the given tasks is 3.15. Thanks to dynamic stopping function, it provides a significant improvement on information transfer rate. The proposed A-BCI algorithm is important in terms of practical BCI systems.

Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.

Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.

Region based Brain Computer Interface for a home control application.

Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

Computerized control of the procedure for detecting and removing airborne particles in operating rooms.

Surgical-site infections are still a major problem in modern medicine. Normal skin fora of patients or healthcare workers causes more than half of all infections following clean surgery, but the importance of airborne particles in this setting remains controversial. The use of ultraclean air in operating rooms has been shown to reduce infection rates significantly. High efficiency particlulate air (HEPA) filters are used in some modern operating rooms. Although the uses of HEPA filters, the air quality should be controlled by another device to make safe the air in operating rooms and intensive care units. In this study, a computerized system was established to control the cleanliness of the air by measuring the presence of airborne particles of varying sizes and numbers in operating rooms. When the maximum values are exceeded, the system warns the authorized people by phone, sound, or displays.

Survey of fuzzy logic applications in brain-related researches.

The aim of this study was to survey fuzzy logic (FL) applications in brain researches. In general, these applications are related to pattern recognition for localization in brain structures or tumor detection, image segmentation, and simulations. In recent years, neural networks and FL are gaining popularity. FL is based on the observation of people. The enormous amount of information representation by the brain suggests that FL principles can be useful, especially for complex brain functions. Causal models based on functional neuroanatomy can be then implemented in computer simulations to reflect the dynamical intersection of brain structures. FL is considered as an appropriate tool for modelling and control. FL has been applied in different ways to brain researches. This paper surveys the utilization of FL in brain researches.