Visual field impairment affects more than 100 million people globally. However, due to the lack of the access to appropriate ophthalmic healthcare in undeveloped regions as a result of associated costs and expertise this number may be an underestimate. Improved access to affordable diagnostic software designed for visual field examination could slow the progression of diseases, such as glaucoma, allowing for early diagnosis and intervention. We have developed Specvis, a free and open-source application written in Java programming language that can run on any personal computer to meet this requirement (http://www.specvis.pl/). Specvis was tested on glaucomatous, retinitis pigmentosa and stroke patients and the results were compared to results using the Medmont M700 Automated Static Perimeter. The application was also tested for inter-test intrapersonal variability. The results from both validation studies indicated low inter-test intrapersonal variability, and suitable reliability for a fast and simple assessment of visual field impairment. Specvis easily identifies visual field areas of zero sensitivity and allows for evaluation of its levels throughout the visual field. Thus, Specvis is a new, reliable application that can be successfully used for visual field examination and can fill the gap between confrontation and perimetry tests. The main advantages of Specvis over existing methods are its availability (free), affordability (runs on any personal computer), and reliability (comparable to high-cost solutions).

Alternating current stimulation is a promising method for the study and treatment of various visual neurological dysfunctions as well as progressive understanding of the healthy brain. Unfortunately, due to the current stimulation artifact, problems remain in the context of analysis of the electroencephalography (EEG) signal recorded during ongoing stimulation. To address this problem, we propose the use of a simple moving average subtraction as a method for artifact elimination. This method involves the creation of a template of the stimulation artifact from EEG signal recorded during non-invasive electrical stimulation with a sinusoidal alternating current. The present report describes results of the effects of a simple moving average filtration that varies based on averaging parameters; in particular, we varied the number of sinusoidal periods per segment of the recorded signal and the number of segments used to construct an artifact template. Given the ongoing lack of a mathematical model that allows for the prediction of the “hidden” EEG signal with the alternating current stimulation artifact, we propose performing an earlier simulation that is based on the addition of artificial stimulation artifact to the known EEG signal. This solution allows for the optimization of filtering parameters with detailed knowledge about the accuracy of artifact removal. The algorithm, designed in the MATLAB environment, has been tested on data recorded from two volunteers subjected to sinusoidal transorbital alternating current stimulation. Analysis of the percentage difference between the original and filtered signal in time and frequency domain highlights the advantage of 1-period filtration.