Neural networks are used as triggers at highenergy physics detectors. These triggers can separate the event that must be saved for later analysis from the other events or noises. Using the raw data of the detector, the signal and the background can be separated offline. After separation, sets of signals and backgrounds can be used to train the neural network. A gas-filled detector (multiwire proportional chamber) was used to study the trigger at different noise levels to find the most stable neural network that tolerates the random hits. The ratio of the recognized and the unrecognized signal and background events is used for the measurement. Its stability is part of the systematical uncertainty.
Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.