Brain Waves and Evoked Potentials as Biometric User Identiﬁcation

Strategy: An Affordable Low-cost Approach

Roberto Saia, Salvatore Carta, Gianni Fenu and Livio Pompianu

Department of Mathematics and Computer Science,

University of Cagliari, Via Ospedale 72 - 09124 Cagliari, Italy

Keywords:

Security, Biometric, Brain Waves, Electroencephalography, EEG, Evoked Potentials, EP, Muse.

Abstract:

The relatively recent introduction on the market of low-cost devices able to perform an Electroencephalogra-

phy (EEG) has opened a stimulating research scenario that involves a large number of researchers previously

excluded due to the high costs of such hardware. In this regard, one of the most stimulating research ﬁelds

is focused on the use of such devices in the context of biometric systems, where the EEG data are exploited

for user identiﬁcation purposes. Based on the current literature, which reports that many of these systems

are designed by combining the EEG data with a series of external stimuli (Evoked Potentials) to improve the

reliability and stability over time of the EEG patterns, this work is aimed to formalize a biometric identiﬁca-

tion system based on low-cost EEG devices and simple stimulation instruments, such as images and sounds

generated by a computer. In other words, our objective is to design a low-cost EEG-based biometric approach

exploitable on a large number of real-world scenarios.

1 INTRODUCTION

The introduction on the market of low-cost devices

capable of detecting brain waves with relative ac-

curacy has opened numerous research paths rang-

ing from canonical domains (Xavier et al., 2021;

Prathibha et al., 2017) to completely new ones, such

as those oriented to the deﬁnition of biometric sys-

tems (Nakanishi and Maruoka, 2019). The grow-

ing number of such devices is accompanied by an

equally increasing availability of applications and li-

braries to support the development of new applica-

tions: a representative example is that of the Muse

(https://choosemuse.com) device, marketed together

with an application aimed at supporting meditation

activities, and widely used in literature for numerous

experiments, also thanks to a large number of pro-

gramming libraries available.

More formally, these devices perform an Elec-

troencephalogram (EEG) (Thakor and Sherman,

2013) by measuring the electrical activity of the brain

using a series of electrodes positioned on the scalp.

In this regard, it should be added that the number

of these electrodes is greater in professional devices,

where even the positioning requires more attention,

requiring also the use of a conductive paste, differ-

ently from the low-cost devices that use a smaller

number of electrodes, which can usually do not re-

quire any conductive paste. However, in both cases

the positioning of the electrodes follows the 10-20

System of Electrode Placement (Homan et al., 1987)

formalization shown in Figure 1.

Such a system indicates the relationship between

each electrode location and the underlying cerebral

cortex area. In order to guide in the correct position-

ing of the electrodes, each position reports the lobe

and the hemisphere indication, i.e., the C, F, P, O, and

T letters indicate, respectively, the Central, Frontal,

Parietal, Occipital, and Temporal lobe (the Central

lobe is used only for identiﬁcation purposes, as it does

not actually exist). Similarly, the even numbers 2, 4,

6, 8, and 10 denote the right hemisphere, and the odd

numbers 1, 3, 5, 7, and 9 the left one; the z letter indi-

cates a median-line electrode (the smaller is a number,

the closer it is to the median line); the Nasion label

denotes the area between the forehead and nose and

Inion label denotes the area at the back of the skull.

At the time of writing, the most popular low/medium

cost EEG signal acquisition devices are those reported

in Table 1, which also provides information about the

data resolution in bits and the number of electrodes.

Based on the cost and features, as well as the

availability of development tools, among the afore-

mentioned devices we have chosen for this work the

InteraXon Muse-2, a device marketed as a tool that

helps in reaching a deep relaxed state, assisting the

614

Saia, R., Carta, S., Fenu, G. and Pompianu, L.

Brain Waves and Evoked Potentials as Biometric User Identiﬁcation Strategy: An Affordable Low-cost Approach.

DOI: 10.5220/0011297600003283

In Proceedings of the 19th International Conference on Security and Cryptography (SECRYPT 2022), pages 614-619

ISBN: 978-989-758-590-6; ISSN: 2184-7711

people during a meditation session, exploiting an au-

ral feedback related to the detected brain activity. It

uses ﬁve electrodes placed on a headband that, on the

basis of the placements of Figure 1, are: a reference

electrode (NZ) and four acquisition electrodes (TP9,

AF7, AF8, and TP10). All the Muse-2 functions can

be managed through an application released for the

iOS 12.2 and Android 8 or higher operating systems

but a huge number of applications and libraries are

also available on the Internet aimed to extend its fea-

tures, allowing the researchers to create customized

applications using libraries designed for several pro-

gramming languages. Some representative examples

are: Brainﬂow (https://brainﬂow.org), a Python li-

brary that offers API able to ﬁlter, parse, and ana-

lyze the EEG data; Brains@play(https://brainsatplay.

com), an open-source framework that allows the de-

velopers to create brain-responsive applications based

on the web technologies; Muse LSL (https://github.

com/alexandrebarachant/muse-lsl), a Python package

that provides functions for streaming, visualizing, and

recording the EEG data. Among those available, in

this work we have chosen to use Muse LSL, which op-

erates under the Linux operating system, allowing us

to perform a Bluetooth Low Energy (BLE) commu-

nication between the computer and the Muse device

(compatible with Muse 1, Muse 2, and Muse S), pro-

viding functions for managing and testing the device

connection, as well as a Python library (working with

both Python versions 2.7 and 3.x) for developing the

code. It should be added that the Lab Streaming Layer

(LSL) adopted by Muse LSL is a mechanism widely

used for the uniﬁed collection of time-series measure-

ments in the scientiﬁc ﬁeld since it allows us the syn-

chronization of the streaming data for real-time anal-

ysis or recording. Based on the results of other stud-

Table 1: Low-cost EEG Devices.

Brand Product Resolution Electrodes Reference

name name bits number site

Emotiv Insight 14 05 https://www.emotiv.com

Emotiv Epoch X 14/16 14 https://www.emotiv.com

Emotiv Epoch Flex 14 32 https://www.emotiv.com

InteraXon Muse-2 12 04 https://choosemuse.com

InteraXon Muse S 12 04 https://choosemuse.com

Neurowsky MindWave Mobile 2 12 01 http://neurosky.com

OpenBCI Cyton Biosensing Board 24 08 https://openbci.com

ies in this regard, the idea behind this work is related

to the exploitation of external stimuli to induce reli-

able/stable EEG patterns, with the aim to exploit them

as a biometric approach for user authentication. Dif-

ferently from the solutions in the literature, which are

usually unsuitable for large-scale use in the real-world

scenarios due to a series of limitations mainly related

to the need for expensive and/or complex EEG data

acquisition and stimulation techniques, the proposed

system is designed to use simple and low-cost hard-

ware (i.e., one of those in Table 1) and stimulation

techniques (i.e., computer sounds and images).

2 BACKGROUND AND RELATED

WORK

The human brain is made up of billions of neurons,

each of which can potentially connect with thousands

of other neurons in order to establish communication

channels through low intensity electric voltages (in

the order of microvolts). This type of electrical ac-

tivity of neurons generates brain waves, which on the

basis of their frequency (in the range from 4 to 100

Hz) are classiﬁed into ﬁve categories, each of them

denoted by a Greek letter: the Delta wave less than 4

Hz, the Theta wave from 4 to 8 Hz, the Alpha wave

from 8 to 12 Hz, the Beta wave from 13 to 30 Hz, and

the Gamma wave greater than 30 Hz. Each brain area

is then characterized by different wave frequencies,

which can be generated simultaneously. Several stud-

ies demonstrated the existence of a strong correlation

between brain wave rhythms and brain state such as,

for instance, between the fastest rhythms and the brain

processing of information, and between the slowest

rhythms and the inactive brain state (Sterman, 1996).

Nowadays, a growing number of literature works

are focused on the exploitation of EEG signals as a

biometric approach for the users identiﬁcation (Yang

and Deravi, 2017), proposing approaches/strategies

able of producing unique and stable patterns over

time. In this context, it is possible to ﬁnd works aimed

at formalizing these identiﬁcation systems in a practi-

cal way (Thomas and Vinod, 2018), along with works

that instead deal with issues and aspects connected to

this research area (Fraschini et al., 2019). It should

be noted that to improve the stability and reliability

of the EEG patterns, an increasing number of works

adopt external stimuli of different types, although in

many cases these works do not propose a practical for-

malization of these systems usable in real-world con-

texts. This usually depends on a series of limitations

such as, for example, the long data EEG acquisition

times (Jayarathne et al., 2017).

A signiﬁcant example of the above scenario is this

work (Campisi and La Rocca, 2014), where the au-

thors analyzed the brain activity for the automatic user

recognition purpose, as well as in this work (Soni

et al., 2016), where the authors instead propose a sys-

tem that allows users to set a pattern of brain waves

to perform the same task, combining eye blink, at-

tention, and the Alpha, Beta, Theta, and Delta brain

rhythms. Another signiﬁcant work (Nakanishi and

Hattori, 2017) exploits the EEG activity evoked by

Brain Waves and Evoked Potentials as Biometric User Identiﬁcation Strategy: An Affordable Low-cost Approach

615

Figure 1: 10-20 System of Electrode Placement.

invisible visual stimulation as biometric approach,

whereas this work (Reshmi et al., 2016) proposes an

approach to brain biometric user recognition, and in

this one (Abed and Abed, 2020) the authors formal-

ize an authentication system based on the features of

two brain waves, Gamma and Beta.

Evoked Potentials: An Evoked Potential (EP) (Walsh

et al., 2005) is an electrical potential that is mea-

sured in an area of the nervous system, such as the

brain, as a result of an external stimulus. These stim-

uli can be of different types, the most commonly

used are the Auditory Evoked Potentials (AEPs) (Seha

and Hatzinakos, 2018) (acoustical stimuli such as

a short sound) and the Visual Evoked Potentials

(VEPs) (Zhao et al., 2021) (visual stimuli such as a

light ﬂash). The EPs are widely used to evaluate the

electrical activity related to speciﬁc areas of the brain

or the spinal cord, this in order to diagnose neurolog-

ical problems, and in the case of the brain that magni-

tude is in the order of a few microvolts at most.

An example of these approaches is the Inter-

mittent Photic Stimulation (IPS) (Coull and Pedley,

1978), where a pair of glasses capable of emitting in-

termittent light are used, or devices capable of gener-

ate sound stimuli with different frequencies, such as

in this study (Di et al., 2018), where the authors exper-

imented the effect of intermittent pure tones at 50 and

6 phon (i.e., a logarithmic unit of loudness level for

tones and complex sounds), using as frequency 125,

250, 500, 1000, and 4000 Hertz, and a duration of 10

seconds, demonstrating the relationships between the

acoustic properties of stimuli and the EEG activity.

Other studies in the literature instead ex-

perimented the effect of the so-called binaural

sounds (Colburn and Durlach, 1978), which are based

on the brain perception of interaural differences dur-

ing binaural stimulus (Blauert, 2013). In more detail,

this technique inducts the brain to interpret as a single

tone two different tones applied on the left and right

ears (formally deﬁned carrier-tone and offset-tone),

where the detected single tone is given by the differ-

ence between the frequencies of these two ones. An

interesting literature work (Rajan et al., 2018) eval-

uates the impact of binaural sounds in terms of their

positive and negative impact in areas such as health-

care, security, education, and entertainment.

A recent work in literature (Zhao et al., 2021)

compares the performance of three different VEP sig-

nals in the context of a VEP-based biometric user

identiﬁcation system, whereas another work (Rosli

et al., 2021) takes into account the use of EEG data

with visual stimuli for the same purpose, using the

Wavelet Packet Decomposition (WPD) technique in

the feature extraction process. With regard to the bio-

metric user identiﬁcation systems based on EEG data

and auditory stimuli, in this work (Mukai and Nakan-

ishi, 2020), the authors use ultrasound stimuli for the

EEG stimulation, with the aim to avoid that the users

can be distracted from their current activity during

the identiﬁcation process. For the sake of complete-

ness, it should be added that although visual and audi-

tory stimuli are the most commonly used in literature,

some works use other type of stimuli such as, for in-

stance, those based on vibrations (Nakashima et al.,

2021).

However, it should be added that all the aforemen-

tioned approaches, as well as most of the others in

the literature, do not have characteristics of perfor-

mance, simplicity, and cost that make them suitable

for widespread use as a biometric system.

Open Problems: Regardless of the technique/strat-

egy used to deﬁne a biometric system based on EEG

data, there are some well-known problems that re-

duce its practical feasibility, such as: the Data Com-

plexity, which is given by the fact that the EEG data

are complex, non-linear, and non-stationary (Mahato

and Paul, 2019); the Data Heterogeneity, which de-

pends on the fact that considerable differences exist

in the EEG data related to different users (Jausovec,

1997); the Data Calibration, which is related to the

need of a system calibration before the EEG data ac-

quisition (Jayarathne et al., 2017); the Data Diversity,

which depends on the difﬁculty of obtaining the same

EEG patterns over time, due to multiple factors that

inﬂuence users (Kaur et al., 2017).

Evaluation Metrics: The evaluation metrics largely

adopted in the literature to evaluate the performance

of the biometric systems based on the EEG data are

mainly based on two rates, the False Acceptance Rate

(FAR) and the False Rejection Rate (FRR) (Dahel and

Xiao, 2003), which express, respectively, how many

times a user is erroneously allowed access, and how

many times a legitimate user is erroneously denied

SECRYPT 2022 - 19th International Conference on Security and Cryptography

616

access. Another widely used metric based on the

aforementioned two ones is the Half Total Error Rate

(HTER), which is calculated as HT ER =

(FAR+FRR)

whereas the point at which we have the intersection

of the FAR and FRR values is named Equal Error

Rate (EER). The percentage of users correctly identi-

ﬁed over their total number is the Correct Recognition

Rate (CRR) metric is calculated as TAR=1-FRR.

Research Motivation: This work stems from the ob-

servation that, unlike other biometric user identiﬁca-

tion approaches (e.g., facial recognition, ﬁngerprint

recognition, retinal recognition, etc.), those based on

EEG data are characterized in literature by a more the-

oretical than practical formalization, due to some in-

trinsic limitations. The most important of these are

certainly the difﬁculty of obtaining EEG patterns that

well characterize the user and that are stable over

time, as well as a series of problems related to the

preliminary conﬁguration of the biometric system and

the related acquisition time. In light of this obser-

vation, this work is aimed at formalizing a low-cost

biometric identiﬁcation system based on EEG data

detected during an external stimulation of the users,

which is practically usable in the real-world scenar-

ios, although with some limitations, in order to pro-

vide a starting point for subsequent improvements.

3 APPROACH FORMALIZATION

The idea behind the proposed approach is mainly

based on the differential analysis of EEG data

recorded before and after the application on the sub-

ject of a series of stimuli: practically, we capture

the brain waves activity in a deﬁned time-frame, op-

erating without the external stimuli for the half of

time, and with them in the remaining time. We take

into consideration all the brain waves types, read

data from the Muse LSL stream, compute the av-

erage power of a signal in each speciﬁc frequency

range in order to reduce the noise, according to the

most widely used approach for the EEG data analy-

sis, where the sensors data is decomposed into dis-

tinct frequency bands, i.e., δ = [0.5, 4] Hz, θ = [4, 8]

Hz, α = [8, 12] Hz, β = [12, 30] Hz, and γ = [30, 100]

Hz. The band power is calculated computing the one-

dimensional Discrete Fourier Transform (DFT) using

the Fast Fourier Transform (FFT) algorithm, which

returns, for each frequency, a complex number that

allows us to extract amplitude and phase of the signal

at the desired frequency. The experimental environ-

ment in terms of the software and hardware will be

composed by the elements reported in Table 2.

The steps that compose the proposed approach,

Table 2: Experimental Environment.

Type Version Details Reference

Experimental Intel CPUs 3.40 GHz×8

Workstation i7-6700 RAM 16 GB

EEG Muse Version 2

device Headband https://choosemuse.com

Operating Linux 64 bit https://www.debian.org

system Debian 11 Kernel 5.10.0-9 https://www.kernel.org

Programming Python Version 3.9.2 https://www.python.org

language/IDE Eclipse PyDev Version 9.2.0 https://www.pydev.org

Python AccelBrainBeat Version 1.0.5 pypi.org/project/AccelBrainBeat

libraries for Scikit-learn Version 1.0.1 https://scikit-learn.org

EEG and eeglib Version 0.4.1 https://github.com/Xiul109/eeglib

external Muse LSL Version 2.1.0 https://github.com/

stimuli alexandrebarachant/muse-lsl

whose high-level architecture is shown in Figure 2,

are reported in the following:

- Acquisition System Calibration: veriﬁcation of the

correct positioning of the Muse-2 device after the

user wore the headband; this is performed by mea-

suring the signal activity on each headband elec-

trode using the Muse LSL library functionalities;

- Unstimulated EEG Data Collection: collection of

the EEG data for half of a experimentally deﬁned

time-frame, without applying any external stimulus

during the data acquisition; in this phase the signals

relating to all the brain waves will be acquired, the

most characterized of which will be selected later;

- Stimulated EEG Data Collection: collection of the

EEG data for the remaining period of the time-

frame, applying a series of external stimuli; the na-

ture and type of stimuli will be deﬁned experimen-

tally using both visual (VEP) and auditory (AEP)

stimuli at different intensities and frequencies;

- Data Transform and Analysis: elaboration of the

collected data through the FFT algorithm, prepro-

cessing techniques, and differential analysis of the

EEG patterns; the EEG data collected with and

without the external stimuli are compared in order

to extract information that characterizes the way in

which the user responds to external stimuli;

- Data Comparing and Classiﬁcation: the obtained

patterns are compared to the database of existing

ones with the aim to identify the users; a tolerance

margin is experimentally deﬁned in this phase.

Future Direction: According to the experimental

steps previously mentioned, we want to investigate

the inﬂuence on the EEG signal of simple stimuli gen-

erated through ﬂashing of the computer screen and

environmental sounds. In other words, differently

from the work in the literature we want to explore the

feasibility of a system realized using only low-cost

EEG device and simple stimuli that do not require ad-

ditional hardware.

In this regard, although this work proposes only a

theoretical formalization, a series of preliminary ex-

periments was carried out on a small number of users,

the results of which indicate the feasibility of the idea

Brain Waves and Evoked Potentials as Biometric User Identiﬁcation Strategy: An Affordable Low-cost Approach

617

Figure 2: High-level Approach Architecture.

behind the proposed system, showing a greater char-

acterization of the user and a better stability over time

of the EEG patterns obtained through a comparative

analysis of the EEG data collected before and after

the application of an external stimulus (ﬂashing of

the computer screen), then without using dedicated

hardware for its generation. These results must sub-

sequently be veriﬁed and validated in depth using a

signiﬁcant number of users, as well as concerning

different stimul and data analysis and classiﬁcation

techniques. Besides deﬁning a low-cost affordable

EEG-based biometric approach to user identiﬁcation,

our work will be facing the open issues previously

described by experimenting with techniques already

used in some previous works, such as, the feature

engineering (Carta et al., 2020; Heaton, 2016), the

feature space transformation (Saia et al., 2020), the

training data decomposition (Saia et al., 2021), and

the data enrichment/discretization (Saia et al., 2019a;

Saia et al., 2019b). An automatic data calibration al-

gorithm will be also developed to reduce the time of

the initial process of data recognition, in the context

of the Muse-2 device functionalities will be exploited.

The data diversity will be instead faced by the adop-

tion of differential data patterns given by the compar-

ison of the data collected before and after the applica-

tion of external stimuli.

4 CONCLUSIONS

This work aimed to verify the existence of conditions

that justiﬁed subsequent experimentation for practi-

cally formalizing a biometric user identiﬁcation sys-

tem based on EEG data acquired under the effect of

external visual stimulation, to face some well-known

problems related to the difﬁculty to obtain stable char-

acterizing EEG patterns. The performed study of

the current literature and the results obtained in our

preliminary experimentation indicate the existence of

such conditions, albeit with limitations whose impact

will be quantiﬁed through future experiments.

Unlike most of the works in the literature, this

work proposes an affordable biometric system based

on low-cost hardware for the detection of EEG sig-

nals, and on an approach of generating external stim-

uli based on existing devices (i.e., the computer

screen), therefore without the need to use dedicated

devices for this purpose (e.g., ﬂash-emitting glasses).

This combination was designed to allow us a wide

use of the system that will be used in many real-

world contexts, perhaps combined with other differ-

ent widespread biometric approaches in order to face

the EEG limitations, improving the reliability of the

biometric system.

ACKNOWLEDGEMENTS

This research is partially funded and supported by:

project “Studio per l’adeguamento di aree portale

per tematismo - BRIC INAIL 2019 - FENU” CUP

F24G20000100001”; “PON R&I 2014-2020 Action

IV.6 - CUP F25F21002270003”.

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