In recent years, due to an exponential increase in mobile game users, automatic recognition of personal traits i.e., gender and user identity is gaining significance. Personal trait recognition also plays a vital role in information security. Traditional approaches for personal trait recognition, including vision and voice-based schemes, are subjected to privacy constraints. To address these challenges, physiological signals such as electroencephalogram (EEG) and electrocardiogram have been utilized in information security for bio-metric and personal attribute identification. Herein, we propose a new technique for EEG-based gender recognition during mobile game play. Towards this, EEG data of 38 users are recorded using the Muse headband while playing a mobile game named Traffic Racer. Different time and frequency-domain features are extracted from the acquired EEG data. Three classification algorithms, i.e., K-nearest neighbor (KNN), random forest, and the Naive bayes, are used to train and validate the proposed model. In particular, KNN achieved an accuracy of up to 97.36% in gender recognition, which shows the efficacy of the proposed approach when compared with other techniques.