Brain signal processing is of importance not only the physiologist doing analysis and investigation and the clinician inspecting patients but also to the biomedical engineer who is responsible for collecting, processing, and interpreting the electroencephalogram (EEG) signals by modeling systems and algorithms for their manipulations. The abundant materials on the subject of brain signal/image processing are scattered in different scientific, technological and physiological journals, international conference proceedings, and also in various databases. Therefore, it is altogether a difficult, too time-consuming and much tiresome work, exclusively to the newcomers in this field. Therefore, this article focuses on providing the list of popular databases available belong to the neurological signals, brain signal/image collections, etc. The count and the kinds of attacks across the networked computer systems have hiked the significance of computer network security. At present, network administrators use to inspect, examine, scrutinize, review, and analyze the network traffic to figure out what is going on and to set up a prompt response in the event of an identified attack. This study analyzes the different sweep techniques such as Ping sweep, TCP sweep, and Null sweep on the popular databases about the brain signal/image collections. The results of the Ping sweep support status, TCP sweep times, and Null scan times on different servers are discussed finally.

Alcoholism is an adverse situation that changes the functioning of important part of nervous system which is neuron. This changes the functional behaviour of alcoholic person. The diagnosis of this state is done with the help of EEG signals which gets modified with the electrical activity of the brain. The EEG datasets used in this paper are taken from University of California, Irvine, Knowledge discovery and databases (UCAKDD). A review on how the EEG signals get affected by the Consumption of Alcohol (COA) and the extraction of features from these signals help to differentiate alcoholic and uninfluenced people with the help of Graphical User Interface (GUI) is presented in this paper. GUI is an interface that showcases the features extracted from the raw EEG data and classifies the two different classes. This is achieved with the help of Sample Entropy, Approximate Entropy, Mean and Standard deviation of raw EEG data collected from the electrodes Frontal Polar(FP), Frontal(F), Central(C). This GUI system is economical and efficient which is used as a proper clinical decision support system by clinicians and also helps rehabilitation centres in getting to know about the subject. Quadratic SVM gives a highest accuracy of 95% for the detection of alcoholic EEG signal.