© 2022 Elsevier LtdElectroencephalography (EEG) as a biometric modality has gained considerable interest in recent years. Many state-of-the-art methods have focused on increasing the recognition accuracy. However, the more complex and manipulative the methods become, the less practical and generalized they are in real-life applications. In this study, we prioritized computational efficiency and evaluated the model performance. In this direction, we propose the mean curve length (MCL), a simple measure quantifying signal complexity, which is analytically and empirically related to the Katz fractal dimension. By merely being the average of the absolute value of the first-order difference of a signal, MCL is arguably the most computationally efficient feature that can be extracted from an EEG signal. In this paper, we utilized it for person identification and authentication on a large standard dataset comprising 109 subjects under the eyes-open (EO) and eyes-closed (EC) resting state conditions. We employed a Mahalanobis distance-based classifier both for identification and authentication tasks. Our results indicate that in addition to its simplicity and low computational cost, MCL provides a remarkably high individual distinction as well. Specifically, recognition accuracies were 99.4% (EO) and 98.8% (EC) for identification, and for authentication, equal error percentages of 6.33% (EO) and 10.50% (EC) were obtained. Our study offers a fast and accurate neural biometric recognition scheme promising especially for practical real-world and real-time applications. It further proves the effectiveness of nonlinear signal measures in individual discrimination, and promotes shifting the focus beyond the conventional brain oscillatory and connectivity measures commonly fostered in EEG-based biometrics literature.