MindID

Abstract

Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., anti-surveillance prosthetic masks can thwart face recognition, contact lenses can trick iris recognition, vocoder can compromise voice identification and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the user's brainwave signals for identification and offers a more resilient solution, has recently drawn a lot of attention. However, the state-of-the-art systems cannot achieve similar accuracy as the aforementioned methods. We propose MindID, an EEG-based biometric identification approach, with the aim of achieving high accuracy and robust performance. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Next, the decomposed Delta signals are fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns varying attention weights to different EEG channels based on their importance. The discriminative representations learned from the attention-based RNN are used to identify the user through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the results illustrate that our model achieves an accuracy of 0.982 and significantly outperforms the state-of-the-art and relevant baselines. The second local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be widely deployed in practical settings.

Multi-Factor Authentication: A Survey

“…Examples could be found in [228][229][230] where conventional factors, such as iris, retina, fingerprints, etc., are considered. Utilizing neural networks for the next-generation biometrics is the most likely way to proceed due to presently high levels of the analysis complexity [231,232].…”

Electroencephalographic (EEG) based Deep Learning (DL): A Comparative Review

“…Physionet data set used by the ref. [4][5][6], [11], [17], [20], [23], [25], [27][28], [33], and [37][38][39] and work on the publicly available dataset consisting of EEG of 109 participants completing various motor/imagery duties, it's a popular benchmark for biometric with EEG. In [9] datasets from four different experiments measuring endogenous brain functions (driving fatigue and emotion) in addition to time-locked artificially created brain responses from 157 subjects, [5] datasets including emotion and combined data.…”

“…Additionally, the signals recorded by EEG systems must be carefully analyzed and interpreted to get useful data regarding the brain's electrical activity. In [2], [4][5][6], [9], [11], [14], [16][17][18][19], [20], [23], [25], [27][28], [30], [33], and [37][38][39] worked on BCI2000 system to record and analyzed EEG using 32 electrodes, while [1][8] [34][40] used AgCl electrodes EEG signals were recorded using a (Bio semi) Active Two system, EEG data were collected at a 512 sampling rate (Hz), AgCl with 32 electrodes works on the (10-20) of the international systems. Another device was using named GALILEO BE Light amplifier equipped with 19 channels/electrodes.…”

“…[27] use MI, the subjects were only asked to visualize and perform various motor tasks during the EEG signal capture phase. [33], a DL model based on attention was used to extract deep Delta wave representations from brain waves. In [34], use four different types of move imaging, including the tongue, the 2 feet, the right, and left hands, the short video was shown with various emotional classifications, there was a need for a resting stage of (1 min) EO and (1 min) EC.…”

Research on the Analysis of Correlation Factors of English Translation Ability Improvement Based on Deep Neural Network

“…They found that English ⟶ Romanian translations can be considered for parallel sentences of 13–28k in the case of extremely limited resources. They found that English ⟶ Romanian translation can consider 13–28k parallel sentences with extremely limited resources [ 10 ]. Akan et al proposed a speech band extension method based on Convolutional Neural Network (CNN), which is different from previous speech band extension studies and is the first.…”