Brain computer interface as a forensic tool (original) (raw)
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Psychophysiology
Brain Fingerprinting (BFP) is an electroencephalogram‐based system used to detect knowledge, or absence of knowledge of a real‐life incident (e.g., a crime) in a person's memory. With the help of BFP, a potential crime suspect can be classified as possessing crime‐related information (Information‐Present), not possessing crime‐related information (Information‐Absent), or Indeterminate (BFP unable to classify a subject). In the lab setting, we compare the ground‐truth of a subject (i.e., real‐life involvement in an incident) against their classification based on BFP testing. We report two studies: replication of BFP with university students (Study 1) and replication of BFP with parolees (Study 2). In Study 1, we tested 31 subjects (24 females, seven males, mean age = 21.3) on either their own or another subject's real‐life incident. BFP correctly classified nine Information‐Present and 18 Information‐Absent subjects, but with one false positive and three exclusions. In Study ...
BRAIN COMPUTER INTERFACE FOR BIOMETRIC AUTHENTICATION BY RECORDING SIGNAL
Computer Science & Information Technology (CS & IT), 2019
Electroencephalogram(EEG) is done in several ways, which are referred to as brainwaves, which scientists interpret as an electromagnetic phenomenon that reflects the activity in the human brain, this study is used to diagnose brain diseases such as schizophrenia, epilepsy, Parkinson's, Alzheimer's, etc. It is also used in brain machine interfaces and in brain computers. In these applications wireless recording is necessary for these waves. What we need today is Authentication? Authentication is obtained from several techniques, in this paper we will check the efficiency of these techniques such as password and pin. There are also biometrics techniques used to obtain authentication such as heart rate, fingerprint, eye mesh and sound, these techniques give acceptable authentication. If we want to get a technology that gives us integrated and efficient authentication, we use brain wave recording. The aim of the technique in our proposed paper is to improve the efficiency of the reception of radio waves in the brain and to provide authentication. Keyword Related work, EEG brain signal, Brain wave, Overall projcet outline, System requirements. 1. INTRODCTION The evolution of life technology has many requirements. The application of this technology on the ground includes obtaining permission for the person authorized to enter (all these methods depend on the pin technique or password to obtain permission from the system) as in banks, experimental laboratories, nuclear reactor, confidential data in computerized systems ... Houses Smart.As a result of the development of piracy in return for the weakness in the development of authentication techniques, the main purpose of which is to avoid the imbalance that occurs in this aspect.The best solution to avoid these problems is to follow a new approach as in our paper of these biometrics, As a result of the unique efficiency of this road for each user is more effective than others and less prone to the problems mentioned. EEG is one way to measure brain waves of different-frequencies in human brain, By placing the electrodes in specific-places of the scalp (head) in order to identify and record brain waves. These recordings are a collection of the amount of waves produced by neurons, which are estimated in millions of interconnected and adjacent neurons. Here, it should be noted that the EEG is
Biometric Identification using Electroencephalography
2020
In this paper, investigate the use of brain activity for person identification. A biometric system is a technological system that uses information about a person. Research on brain signals show that each individual has a unique brain wave pattern. Electroencephalography signals generated by mental tasks are acquired to extract the distinctive brain signature of an individual. Electroencephalography signals during four biometric tasks, namely relax, math, read and spell was acquired from 50 subjects. Features are derived from power spectral density. Classification is performed using Feed forward neural network and Recurrent neural network. The performance of the neural model was evaluated in terms of training, performance and classification accuracies. The results confirmed that the proposed scheme has potential in classifying the EEG signals. RNN is considerably better with an average accuracy of 95% for the spell task and 92% for the read tasks in comparison with a feed forward neu...
EEG Analysis in Biometric Identification and Authentication
2018
This article demonstrates that EEG (Electroencephalogram) signals can be used as a biometric identifier to authenticate the person. The patterns of brainwave signals are unique for every individual and it is very difficult to use these patterns for person identification and authentication purpose. Although the brainwaves signals are much complicated to study, there are very little studies have been done on the brain wave signals based biometrics. The biometric based on these physiological traits are resistant to frauds in sensitive applications domains. In confidential areas like military organizations, government agencies secrete potential domains and likewise highly restricted areas, the biometric facility based on EEG signals can be very useful. This article represents the strategy behind implementation of EEG based biometric. Here we have achieved success in authenticating the person with the help of wavelet transform. We used 5 channels of EEG waveform as features to authentica...
International Journal of Scientific & Engineering Research, 2017
Electroencephalographic (EEG) recordings and processing open a window to have glance onto the commands running in human brain associated with task or thought. Non-invasive acquisition of EEG data has no harms or potential risks to the subject therefore it is best for the development of brain computer interface (BCI). Recorded brains waves are alternating voltage appearing on the human scalp associated with voluntary (event-related-potentials) and involuntary (artifacts) tasks of the brain. In this manner, the EEG signals existing on the surface of the scalp are in the form of mixture. Features of interest are extracted from the raw data by applying various mathematical models. In this study, non-invasive EEG data recorded from a healthy subject for up movement of right hand is processed using two feature extraction algorithms. This study also introduces a new algorithm based on independent component analysis (ICA) using coefficient of determination. Results obtained by using proposed ICA algorithm are found to be in good agreement with those obtained using EEGLAB.