2,217 research outputs found
PIN generation using EEG : a stability study
In a previous study, it has been shown that brain activity, i.e.
electroencephalogram (EEG) signals, can be used to generate personal
identification number (PIN). The method was based on brainâcomputer
interface (BCI) technology using a P300-based BCI approach and showed that
a single-channel EEG was sufficient to generate PIN without any error for
three subjects. The advantage of this method is obviously its better fraud
resistance compared to conventional methods of PIN generation such as
entering the numbers using a keypad. Here, we investigate the stability of these
EEG signals when used with a neural network classifier, i.e. to investigate the
changes in the performance of the method over time. Our results, based on
recording conducted over a period of three months, indicate that a single
channel is no longer sufficient and a multiple electrode configuration is
necessary to maintain acceptable performances. Alternatively, a recording
session to retrain the neural network classifier can be conducted on shorter
intervals, though practically this might not be viable
Human brain distinctiveness based on EEG spectral coherence connectivity
The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match
score fusio
Design and implementation of a subject identification system based on Electroencephalogram
Biometrics are essential methods of identifying people nowadays. There are many types of biometrics, such as the classic methods for iris, face and fingerprint; but most of these are not robust or secure. Recently, biometrics based on electroencephalogram signals using machine learning algorithms have proven to be one of the highest quality and robust methods. Electroencephalograms have advantages over traditional modalities as they are extremely difficult to reproduce and cannot be captured stealthily from a distance. This work describes a system capable of acquiring real-time electroencephalogram signals, processing them using the PREP pipeline, to clean them and improve performance, and making subject identity predictions from electroencephalogram signals using different artificial intelligence algorithms. The system is portable, robust, low-cost and connected to the network to send the results to a server. It is composed of an acquisition system using an analog-to-digital converter and protection systems for electroencephalogram signals. The system is based on a Raspberry Pi Zero 2W as the computer in charge of performing all the computational work of the artificial intelligence algorithms and managing the different tasks. Several deep learning algorithms have been used and compared in terms of results and performance. The EEGNet model has provided the best results with an accuracy of 86.74% in its predictions. The data input to the model has been preprocessed with the PREP pipeline, which has proven to be effective in the results, as it improves the performance of all models that use it. The system provides a functional device with outstanding results that leads the way for future work and applications
Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical momentâs features
Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054
Feature selection for EEG Based biometrics
Department of Human Factors EngineeringEEG-based biometrics identify individuals by using personal and distinctive information in human brain. This thesis aims to evaluate the electroencephalography (EEG) features and channels for biometrics and to propose methodology that identifies individuals. In my research, I recorded fourteen EEG channel signals from thirty subjects. While record EEG signal, subjects were asked to relax and keep eyes closed for 2 minutes. In addition, to evaluate intra-individual variability, we recorded EEG ten times for each subject, and every recording conducted on different days to reduce within-day effects. After acquisition of data, for each channel, I calculated eight features: alpha/beta power ratio, alpha/theta power ratio, beta/theta power ratio, median frequency, PSD entropy, permutation entropy, sample entropy, and maximum Lyapunov exponents. Then, I scored 112 features with three feature selection algorithms: Fisher score, reliefF, and information gain. Finally, I classified EEG data using a linear discriminant analysis (LDA) with a leave-one-out cross validation method. As a result, the best feature set was composed of 23 features that highly ranked on Fisher score and yielded a 18.56% half total error rate. In addition, according to scores calculated by feature selection, EEG channels located on occipital and right temporal areas most contributed to identify individuals. Thus, with suggested methodologies and channels, implementation of efficient EEG-based biometrics is possible.ope
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