What are the benefits of using EEG as the underpinning for a proof-of-individual network?
The use of biometrics, a pattern recognition system, for identification and verification of individuals, is now a widespread technology. With the shortcomings of classical biometrics like retinal scans and fingerprints among others, it has become necessary to come up with more innovative biometrics that are more secure. Bioelectric signals are an emerging candidate in this field. These are measurable electrical signals of low amplitude. Of interests among these are the electrocardiography and electroencephalography (Campisi, La Rocca & Scarano, 2012). This paper looks into the possible application of EEGs as a biometric and the benefits of its usage.
An EEG, or electroencephalogram, is a tracing of the electrical activity generated by the brain. Scientific research has shown that these wave patterns, produced by the electrical activity of neurons, are unique to individuals and can be used as a biometric (Knight, 2007). These waveforms are recorded through electrodes placed on the scalp. This confers the usage of EEG as a biometric its first advantage. The process of recording the electrical activity of the brain is non-invasive and thus a very safe procedure. Despite the cumbersome nature of the process, its non-invasive nature makes it easily applicable in person identification. In addition to the non-invasiveness of the procedure, the data collection process is largely simple. According to Palaniappan and Mandic (2007), once electrodes are placed correctly on the scalp, the waveforms are traced and the analysis is automated. The equipment is also portable making the process more appealing to use as a biometric.
Perhaps the greatest advantage of EEGs is its very confidential nature. Unlike biometrics like fingerprints and voice traits, an individual’s EEG corresponds to the individual’s mental tasks as dictated by neuronal connections and is very difficult to mimics. The EEG has been shown to be virtually impossible to copy. According to Paranjape et al., (2011) because it depends on the inner mental tasks unique to every individual, it cannot be reproduced by others. This makes the use of EEG effective in person identification. This security is increased by the fact that EEGs can be modified depending on the state of the subject (human detection). The resting EEG waveform is different from that obtained when the subject is mentally engaged and the pattern is different when eyes are open or closed and when open (Taguiam, 2017). This makes copying such information impossible. If this were not enough, the security is further boosted by the change in patterns when under stress. So, unlike fingerprints, retinal scans and voice patterns, an individual cannot be forced to reproduce their mental passphrase (Campisi, La Rocca & Scarano, 2012).
Finally, the use of bioelectrical signals for person identification can help in diagnosing certain abnormalities that may be indicated by an EEG during the person identification process. This confers an additional advantage to the field of medicine. Certain medical conditions including sleep disorders, convulsive disorders and lately tumors of the CNS may be diagnosed using EEGs (Paranjape et al., 2011). Use of EEGs might just increase the detection of such disorders based on incidental EEG findings.
In conclusion, while still under development, the use of EEG in biometric identification has myriads of potential benefits. High on this list is the very secure nature of EEGs as a biometric. Additionally, the collection of subject waveforms is non-invasive and very safe. The EEG machines are portable and their use is likely to increase early diagnosis of brain pathologies like convulsive disorders, sleep disorders, and even brain tumors.
Campisi, P., La Rocca, D., & Scarano, G. (2012). EEG for automatic person recognition. Computer, 45(7), 87-89.
Knight, W. (16th January, 2007). Brain Activity provides novel biometric key. New Scientist. Retrieved from https://www.newscientist.com/article/dn10963-brain-activity-provides-novel-biometric-key/
Palaniappan, R., & Mandic, D. P. (2007). EEG based biometric framework for automatic identity verification. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49(2), 243-250.
Paranjape, R. B., Mahovsky, J., Benedicenti, L., & Koles, Z. (2011). The electroencephalogram as a biometric. In Electrical and Computer Engineering, 2001. Canadian Conference on (Vol. 2, pp. 1363-1366). IEEE.
Taguiam, R. A. (Jan 24th 2017). Brainwaves Cab Be Passwords, Scientists Explain How EEG Authentication Works. Nature World News. Retrieved from https://www.natureworldnews.com/articles/35169/20170124/brainwaves-passwords-scientists-explain-eeg-authentication-works.htm