Mean curve length: An efficient feature for brainwave biometrics

2022-07-01
Yahyaei, Reza
Özkurt, Tolga Esat
Electroencephalography (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.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL

Suggestions

Quality Enhancement of Computed Tomography Images of Porous Media Using Convolutional Neural Networks
Yıldırım, Ertuğrul Umut; Uğur, Ömür; Glatz, Guenther; Department of Scientific Computing (2022-2-11)
Computed tomography has been widely used in clinical and industrial applications as a non-destructive visualization technology. The quality of computed tomography scans has a strong effect on the accuracy of the estimated physical properties of the investigated sample. X-ray exposure time is a crucial factor for scan quality. Ideally, long exposure time scans, yielding large signal-to-noise ratios, are available if physical properties are to be delineated. However, especially in micro-computed tomography ap...
Optimal design of sparse mimo arrays for wideband near-field imaging based on a statistical framework
Kocamış, Mehmet Burak; Öktem, Sevinç Figen; Department of Electrical and Electronics Engineering (2018)
Wideband near-field imaging is an emerging remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to achieve high resolution with reduced hardware complexity and cost. In this thesis, based on a statistical framework, an optimal design method is presented for two-dimensional MIMO arrays in wideband near-field imaging. Differe...
Nonlinear elastic material property estimation of lower extremity residual limb tissues
Tönük, Ergin (Institute of Electrical and Electronics Engineers (IEEE), 2003-03-01)
The interface stresses between the residual limb and prosthetic socket have been studied to investigate prosthetic fit. Finite-element models of the residual limb-prosthetic socket interface facilitate investigation of the mechanical interface and may serve as a potential tool for future prosthetic socket design. However, the success of such residual limb models to date has been limited, in large part due to inadequate material formulations used to approximate the mechanical behavior of residual limb soft t...
Integrated circuit design for flip-chip bonded capacitive micromachined ultrasonic transducers
Maadi, Muhammad; Bayram, Barış; Department of Electrical and Electronics Engineering (2013)
In previous decades, the applications of ultrasound technologies in medical imaging and therapeutic systems have significantly increased. In conventional ultrasound systems, the transducer array is separated from the electronic instrumentation with multicore physical cabling. However, connection cables make the system too bulky and degrade the receive sensitivity in ultrasound 3D imaging applications because of the capacitance of long cables. The interface electronics for phased array ultrasound systems (im...
Statistical disease detection with resting state functional magnetic resonance imaging
Öztürk, Ebru; İlk Dağ, Özlem; Department of Statistics (2017)
Most of the functional magnetic resonance imaging (fMRI) data are based on a particular task. The fMRI data are obtained while the subject performs a task. Yet, it's obvious that the brain is active even when the subject is not performing a task. Resting state fMRI (R-fMRI) is a comparatively new and popular technique for assessing regional interactions when a subject is not performing a task. This study focuses on classifying subjects as healthy or diseased with the diagnosis of schizophrenia by analyzing ...
Citation Formats
R. Yahyaei and T. E. Özkurt, “Mean curve length: An efficient feature for brainwave biometrics,” BIOMEDICAL SIGNAL PROCESSING AND CONTROL, vol. 76, pp. 103664–103673, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97256.