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In this study, a new electrooculography (EOG) based system that provides efficient communication for people suffered from motor neuron diseases is presented. The system consists of two distinct devices. The first device operates as a main unit that is activated by the subject's eye movements. This unit is capable of transmitting 10 different command/state messages. These messages enable subject to choose his/her situation such as "I'm fine", "I feel bad", "I'm hungry" and "I'm thirsty". Commands such as "Come", "Go". The number of messages can be increased. The main unit acquires the EOG signal from the subject. Newly developed analogue and digital signal conditioning interprets the eye movements as specific messages and transmits them to the second unit (receiver) using radio frequency transmitter. The messages related to the subject's demands and situation can be heard from both main and receiver unit speakers. The wireless receiver unit is capable of notifying the patient's command by auditory and visual indicators. The realised device was tested by 2 healthy and 2 ALS patients and confirmed to be successful with 100% performance for sending correct messages. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the design and application of an electrooculogram (EOG) based on an efficient human--computer interface (HCI). Establishing an alternative channel without speaking and hand movements is important in increasing the quality of life for the handicapped. EOG-based systems are more efficient than electroencephalogram (EEG)-based systems in some cases. By using a realized virtual keyboard, it is possible to notify in writing the needs of the patient in a relatively short time. Considering the biopotential measurement pitfalls, the novel EOG-based HCI system allows people to successfully communicate with their environment by using only eye movements. Classifying horizontal and vertical EOG channel signals in an efficient interface is realized in this study. The new system is microcontroller based, with a common-mode rejection ratio of 88 dB, an electronic noise of 0.6 µV (p-p), and a sampling rate of 176 Hz. The nearest neighborhood algorithm is used to classify the signals, and the classification performance is 95%. The novel EOG-based HCI system allows people to successfully and economically communicate with their environment by using only eye movements. [ABSTRACT FROM AUTHOR]

Abstract: To investigate how quickly we can recognize faces, we used the onset of eye blinking as a behavioral response. The mean reaction time for the blink response was 188±38ms which was 80ms faster than the motor response. As expected, the subjects were more correct and faster to distinguish between faces and houses than when responding to particular face emotion and identity. Our results suggested that early visual processing before 150ms is very important in face processing. This corroborates previous reports from EEG/MEG studies and intracranial recordings on the face-related response around 100ms after stimulus onset. Our findings indicated that blinking can be used as a fast and reliable behavioral response. [Copyright &y& Elsevier]