Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Abstract

This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

ECG biometrics: A template selection approach

“…As with other biometrics, each sample of the ECG biometric trait is slightly different, introducing intra-class variability [9]. In ECG biometrics the main sources of variability are [10]: a) Artifacts induced by the acquisition setup, such as power-line noise and low frequency motion-induced baseline wander; b) Heart rate changes, which lead to a compression or expansion of the heartbeat waveform; c) Aging, which leads to physical morphological changes that induce a variation on the heartbeat waveform; and d) Clinical conditions, since if any cardiac event occurs, the shape of the heartbeat waveform may suffer major variations. Example of ECG signals acquired at the fingers: a) Segmented heartbeat waveforms with annotated complexes (P-QRS-T); the black line represents the mean, and the dashed lines the standard deviation, while in dark gray we provide an overlay with all the segmented heartbeats; b) Example of morphological changes to the heartbeat waveform caused by different heart rates.…”

A Generic Framework for EEG-Based Biometric Authentication

“…Alpha waves (8)(9)(10)(11)(12)(13) are emitted when a person is awake but in a relaxed state of mind, perhaps with eyes closed or indulged in meditation or yoga. Beta waves (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are prevalent when the person is awake but in an alert state of mind. These waves are emitted even when the brain is in a state of agony, panic, anger, frustration, tension, stress or depression.…”

“…An electrode records the EEG response around the region it is placed over the scalp. However, the response recorded by that electrode changes significantly even if its position changes by a minuscule fashion [29]. Hence, the positioning of electrodes should be fixed and deterministic during enrollment and every authentication phase.…”

Physiological Signals: The Next Generation Authentication and Identification Methods!?

“…These physiological signals spread from their source through the body to the surface of the skin. Via surface electrodes attached (or close) to the body surface, signals from a broad range of sources can be recorded [17]. For example, from the heart, the electrocardiogram (ECG) can be recorded [18], [17], [19]; the muscles' activity can be recorded through the electromyogram (EMG) [20], [21]; the sweat glands determine the electrodermal activity (EDA) [20], [21]; and also brain activity can be recorded, for example, using EEG [17], [6] and fNIRS [6].…”