Diabetic retinopathy is a disease in diabetic patients that affects the eye. It happens due to damage in the blood vessels of the light-sensitive tissues at the retina. In non-proliferative diabetic retinopathy, tiny changes occur in the blood vessels of the eye. Non-proliferative diabetic retinopathy can trigger macular edema or macular ischemia. In this study proposes the retinal vessel segmentation and vessel quantization on the DRIVE database which is publicly available. The experimental results express the retinal vessel can be effectively detected and segmented.

BackgroundBrain computer interfacing is a system that acquires and analyzes neural signals to create a communication channel directly between the brain and the computer. The EEG records the electrical fields generated by the nerve cells. With the help of Fourier Transformation the EEG signals are classified into four different frequency bands.PurposeThe main purpose of the present paper is to report results related to classification of EEG signals of different people subjected to different conditions.MethodsThe experiment has been done on 10 subjects having activities related to hearing music chosen from categories of patriotic, happy, romantic and sad songs along with relaxation activity. 19 electrodes have been used under (10–20) International Standard. The δ, θ α and β components of EEG signals to these activities have been determined. Different statistical methods including linear discriminate analysis have been tested for classification.ResultsResult of the Linear Discriminant Analysis (LDA) made four groups of all modes (Relaxation, Happy, Sad, Patriotic and Romantic Song) labeled group1, Group2, Group3 and Group4 of all ten electrodes for Delta, Theta, alpha and Beta frequencies.ConclusionThe study may be used for the development of activities induced mood recognition (AIMR) system from the EEG signal.

Based on Linear Discriminate analysis (LDA), Principle Component Analysis we explore the characteristics of multichannel Electroencephalogram (EEG), which is recorded from no of subjects recognizing different numbers displayed on the screen by a GUI software designed in Visual Basic 6. The scaling exponent of each digit is different especially at positions C3 and C4, and at positions O1 and O2. LDA exhibits its robustness against noises in our works. We could benefit more from the results of this paper in designing mental tasks and selecting brain areas in brain-computer interface (BCI) systems. The objective of this system is to report the work done related to sensitivity of EEG signals related to specific thought process. The thought process was chosen to be numbers (0-9). The main objective of this work is the analysis and classification of EEG signals among the men and machines and provide a secure communication interface. EEG recordings of six male right-handed subjects in the age group of (20-25) were taken. The subjects were normal without any mental disorder. They did not have any problem in communicating and had normal vision. All subjects have good knowledge of digits. A simple display system in visual basic is prepared for the project. This system generates random number with interval of 2 s. After every 2 s a random number is displayed on the screen. The recording was captured for 3 min. This process was repeated for five times. The EEG signal has been processed by statistical analysis methods such as LDA and PCA. It was found that the EEG signals are sensitive to thought process. So it is possible to recognize thought process through EEG signals. In our ten digit thought process, we get ten distinct clusters by analyzing EEG signals through statistical technique like LDA and PCA. The recognition rate of LDA is 70 %. The recognition rate of PCA is 37 %. So we R.

Music is referred to as language of emotions. Music induces emotion in the brain. These emotions are subject not only types of music, but also the sensitivity of the person subjected to music. Dissimilar cases of songs as relax, patriotism, happiness, romantic or sadness will induce different types of brain activities generating different EEG signals. EEG signal is applied to measure electrical activity of the brain. These EEG signal contain precious information of the different moods of subject. In this work, we proposed a mood recognition system using EEG signal of Song Induced activity. The main purpose is to analyze alpha rhythm of EEG signal related to the left hemisphere, and right hemisphere regions of the brain. We have selected 10 male subjects in the age group of 20-25. The electrodes placed on the scalp of the subject as per the International 10-20 standard. Each test was conducted for 25 min, with eye closed and each subject was asked to concentrate on the given tasks. In this study, we have created EEG dataset containing data of five mental tasks of ten different subjects. We determine the alpha rhythms in the left hemisphere are more predominant over the right hemisphere for emotions. Thus we conclude that the left region of the brain gives more response to the emotions rather than the right region. Here we reduce the EEG database from brain region to left hemisphere. Further we reduce it to single electrode as F7 which reside in left region. The database generated in our study may be used to interface the brain with computer to mood recognition system. This will have wide varieties of applications in the future. For example, the entertainment industries may use it for composition of songs as per their effect on the brain. This study also shows that alpha power frequency carries useful information related to mood recognition. These features are separated using Linear Discriminate Analysis.

In this study we build a mood recognition system using EEG signal of Song Induced activity. In this we have analyzed alpha EEG powers related to left hemisphere, right hemisphere regions of brains. This has given the significance of different brain region related to emotions. This study successfully achieves the goal to design a system which offers offline mood recognition system. In this study we show that it is possible to recognize the different moods of person using EEG signal. We observe the different brain locations as Left Hemisphere and Right Hemisphere to recognize the significance according to different moods. The alpha powers are more alert during National, Happy, Romantic mood as compared to Sad mood. So it is possible to distinguish these different moods using alpha power values. The distance matrices also shows that it is possible to differentiate the emotions of persons using alpha power values.