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Abstract: The biological response to stress originates in the brain but involves different biochemical and physiological effects. Many common clinical methods to assess stress are based on the presence of specific hormones and on features extracted from different signals, including electrocardiogram, blood pressure, skin temperature, or galvanic skin response. The aim of this work is to assess stress using EEG based variables obtained on 4 sportsmen during their activity. In this work, deep neural networks are used to identify stress in a sportsman by analyzing electro-encephalography (EEG) signals. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP). A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle method. MATLAB tool has been used to evaluate existing and proposed system performance