This document presents the development of an EEG-based biometric authentication and security system. It involves acquiring EEG signals from the scalp, preprocessing the raw signals by filtering and artifact removal, extracting features from the signals in the time, frequency and time-frequency domains, classifying the features using support vector machines or neural networks, and authenticating individuals based on their unique EEG patterns. The system aims to provide a more secure alternative to conventional authentication methods like passwords, RFID, and facial recognition.

1.  Md. Mahmudul Hasan
 Prof. Dr. Engr. Mohiuddin Ahmad
 Department of Electrical and Electronic Engineering, KUET, Khulna-9203, Bangladesh.
ContributionContribution
Working PrincipleWorking Principle Classification & AuthenticationClassification & Authentication
Feature ExtractionFeature Extraction
Development of a EEG-Based Biometric Authentication &
Security System
Motivation and ObjectivesMotivation and Objectives
Development of a biometric
authentication system based on
electroencephalogram(EEG).
 Comparison of EEG based
authentication with latest
technologies such as:
 Radio frequency Identification(RFID)
 Near Field Communication(NFC).
 Face detection using Image
Processing.
 To fill the gap between conventional
authentication system and biometric
authentication.
Step 1: Signal Acquisition
Step 2: Row signal pre-processing
a) Filtering
b) Artifact Removal
Step 3: Feature Extraction
 Time-domain feature extraction (mean, median,
and variance)
 Frequency-domain feature extraction (WT based)
and
 Time-frequency domain feature extraction(WPD
i.e. multi-level filters to analyze the time-
frequency information)
Step 4: Classification
 SVM(support vector machine)
 ANN(Artificial neural Network)
Step 5: Biometric Authentication
Fig 4. Plotting channel spectra
.
Discussion and ConclusionDiscussion and Conclusion
ReferencesReferences
System OverviewSystem Overview
EEG & Human AuthenticationEEG & Human Authentication
Flow ChartFlow Chart
Fig. 1. Flow chart of the EEG based security system.
EEG:
 Short form of “Electroencephalography”.
 EEG is nothing but human
“brainwave signal”.
 EEG brainwave pattern is unique from
one person to another like fingerprint.
 EEG signals are the signature of neural activities.
 It is almost impossible to steal.
 EEG is very difficult to mimic.
 EEG is referred as “Language of Mind”.
1) Something you know:
 Textual password
You have a secret that you know
2) Something you have:
A smart card
Barcode
3) Something you are:
A fingerprint
Iris
EEG signal
 EEG is a unique biometric
identifier.
 EEG is Just like Fingerprint.
 EEG Can not be easily
duplicated.
 Adjustments of the EEG head set
is very much important.
 FIR filter is used to remove linear
trends.
 Individuals with bushy hair don’t
have any problems.
according to the EEG Headset.
 EEG Headset are costlier.
 Effective for strong security
system.
• To proof that EEG signal alone is able to
create a unique pattern for each subject.
• To use simple feature extraction methods.
• To provide simple classification methods
to provide strong evidence.
• To make a novel algorithm with EEG signal
processing.
• To provide unique patterns and identify
people with other human features.
• To build a reliable security system.
Authentication system:
(Three fundamental Factors)
Fig. 2. Raw EEG Signals
Fig 3. plotting channel locations
Fig 5. After FIR Filtering (linear trends removed)
Signal Acquisition: EEG HeadsetSignal Acquisition: EEG Headset
Fig 7. Statistical analysis on the dataset
Fig. 6 : After removing channel artifacts
Channel artifact removalChannel artifact removal
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