A survey on methods and challenges in EEG based authentication

Bidgoly, Amir Jalaly; Bidgoly, Hamed Jalaly; Arezoumand, Zeynab

doi:10.1016/j.cose.2020.101788

Cited by 93 publications

(50 citation statements)

References 70 publications

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“…We would be remiss to not also mention that, while our own motivation comes from a desire to model the latent space for inter-subject variance, our proposed solution (if effective) is also very relevant to a body of research in EEG-based biometrics [30,31].…”

Section: Motivationmentioning

confidence: 99%

“…Here, EEG signals are leveraged to uniquely identify users, typically in the interest of security applications. Most commonly, this is a problem framed in terms of authentication or identification [30]. Authentication strives to identify a single person from an open set (and is ostensibly more desirable [30]), while identification considers options in a closed set of candidates.…”

Section: Motivationmentioning

confidence: 99%

“…Most commonly, this is a problem framed in terms of authentication or identification [30]. Authentication strives to identify a single person from an open set (and is ostensibly more desirable [30]), while identification considers options in a closed set of candidates. We suggest that T-Vectors represent a disambiguation paradigm instead.…”

Section: Motivationmentioning

confidence: 99%

“…The majority of the recordings were made with sampling frequencies of 250 Hz or 256 Hz, though some were higher (typically multiples of this, such as 512 Hz or 1024 Hz ) [17]. We limited our analysis to the version 1.2.0 subset of TUEG, which after rejecting outlying data (see December 3, 2020 10/23 Preprocessing), featured people 7 roughly an order of magnitude larger than any prior work in EEG user identification that we were aware of [30,31].…”

Section: Datasetsmentioning

confidence: 99%

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DN3: An open-source Python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning

Kostas

Rudzicz

2020

Preprint

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We propose an open-source Python library, called DN3, designed to accelerate deep learning (DL) analysis with encephalographic data. This library focuses on making experimentation rapid and reproducible and facilitates the integration of both public and private datasets. Furthermore, DN3 is designed in the interest of validating DL processes that include, but are not limited to, classification and regression across many datasets to prove capacity for generalization. We explore the effectiveness of this library by presenting a general scheme for person disambiguation called T-Vectors inspired by speech recognition. These are single vectors created by typically short, though arbitrary in length, electro-encephalographic (EEG) data sequences that uniquely identify users relative to others. T-Vectors were trained by classifying nearly 1000 people using as little as 1 second-long sequences and generalize effectively to users never seen during training. Generalized performance is demonstrated on two commonly used and publicly accessible motor imagery task datasets, which are notorious for intra- and inter-subject signal variability. According to these datasets, subjects can be identified with accuracies as high as 97.7% by simply adopting the label of the nearest neighbouring T-Vectors, with no dependence on task performed and little dependence on recording session, even when sessions are separated by days. Visualization of the T-Vectors from both datasets show no conflation of subjects between datasets, and indicates a T-Vector manifold where subjects cluster well. We first conclude that this is a desirable paradigm shift in EEG-based biometrics and secondly that this manifold deserves further investigation. Our proposed library provides a variety of essential tools that facilitated the development of T-Vectors. The T-vectors codebase serves as a template for future projects using DN3, and we encourage leveraging our provided model for future work.Author summaryWe present a new Python library to train deep learning (DL) models with brain data. This library is tailored, but not limited, to developing neural networks for brain-computer-interfaces (BCI) applications. There is abundant interest in leveraging DL in the wider neuroscience community, but we have found current solutions limiting. Furthermore both BCI and DL benefit from benchmarking against multiple datasets and sharing parameters. Our library tries to be accessible to DL novices, yet not limiting to experts, while making experiment configurations more easily shareable and flexible for benchmarking. We demonstrated many of the features of our library by developing a deep neural network capable of disambiguating people from arbitrary lengths of electroencephalography data. We identify a variety of future avenues of study for these representations produced by our network, particularly in biometric applications and addressing the variation in BCI classifier performance. We share our model, library and its associated guides and documentation with the community at large.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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DN3: An open-source Python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning

Kostas

Rudzicz

2020

Preprint

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“…In addition to the aforementioned studies, neural activity was also utilized for subject identification using EEG signals recorded during different tasks [18][19][20]. For example, Bajwa and Dantu applied their method to motor imagery EEG signals, which recorded the data set from seven subjects, and obtained a CA rate of 98.46% [21].…”

Section: Introductionmentioning

confidence: 99%

Odor and Subject Identification Using Electroencephalography Reaction to Olfactory

Aydemir¹

2020

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It is certain that the human brain responds to all kinds of inputs such as feeling, sound, light, and odor. However, to the best of our knowledge, limited works have investigated the response of the human brain to different inputs, especially in eyes-open and eyes-closed (EO & EC) conditions. Due to its fine temporal resolution, portability, noninvasiveness, and low set-up costs, electroencephalography (EEG) is one of the most practical way to evaluate the response of the brain to different inputs. In this study, the brain reactions to olfactory were analyzed, and two identifications were done, which were odor and subject. The brain reactions were captured by EEG from five healthy subjects during smelling of valerian, lotus flower, cheese, and rosewater odors in EO & EC conditions. We tested band power, statistical data, Hjorth parameters, and autoregressive model features and achieved the highest average classification accuracy rates of 96.94% and 99.34% for odor and subject identifications, respectively. The obtained results proved that the olfactory response of the human brain in EO & EC conditions can be reliably used for odor and subject identifications.

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I Can Think Like You! Towards Reaction Spoofing Attack on Brainwave-Based Authentication

Chiu

Meng

2021

Lecture Notes in Computer Science

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In the coming period of Internet of Things (IoT), user authentication is one important and essential security mechanism to protect assets from unauthorized access. Textual passwords are the most widely adopted authentication method, but have well-known limitations in the aspects of both security and usability. As an alternative, biometric authentication has attracted much attention, which can verify users based on their biometric features. With the fast development of EEG (electro-encephalography) sensors in current headsets and personal devices, user authentication based on brainwaves becomes feasible. Due to its potential adoption, there is an increasing need to secure such emerging authentication method. In this work, we focus on a brainwave-based computer-screen unlock mechanism, which can validate users based on their brainwave signals when seeing different images. Then, we analyze the security of such brainwave-based scheme and identify a kind of reaction spoofing attack where an attacker can try to imitate the mental reaction (either familiar or unfamiliar) of a legitimate user. In the user study, we show the feasibility and viability of such attack.

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A survey on methods and challenges in EEG based authentication

Cited by 93 publications

References 70 publications

DN3: An open-source Python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning

DN3: An open-source Python library for large-scale raw neurophysiology data assimilation for more flexible and standardized deep learning

Odor and Subject Identification Using Electroencephalography Reaction to Olfactory

I Can Think Like You! Towards Reaction Spoofing Attack on Brainwave-Based Authentication

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