It is estimated that there are millions of people with epilepsy around the world. Seizure detection and prediction systems are built to improve lifestyle of patients. Closed-loop systems are designed to predict and detect seizures and inform patient and caretakers. Ideally, wireless technologies are used in order not to interfere with patient's life. We build a prototype for closed-loop systems consisting of Mind Wave EEG capturing device and Android application communicating via Bluetooth. The application can store signals locally or send them to cloud and then process them for different applications such as BCI, Neurofeedback, epileptic seizure prediction, etc.

Nonintrusive load monitoring (NILM) is a procedure for the analysis of the changes in the power (current and voltage) that goes into households and classifying the appliances used in the house according to their individual energy consumption. Utility companies use smart electric meters accompanied with NILM to examine the particular uses of electric power in households. Focus of this paper is on the analysis of the “ACS-F2 Database of Appliance Consumption Signatures”. The challenge lies in predicting the states of the electrical devices based on the measuring data which had been previously stored. Machine learning techniques have demonstrated to be effective in classification and pattern recognition tasks. In this paper, different algorithms implemented in the WEKA software are going to be used for the classification.