references.bib

@inproceedings{Akkermans2005,
abstract = { In this paper we investigate how the acoustic properties of the pinna-i.e., the outer flap of the ear- and the ear canal can be used as a biometric. The acoustic properties can be measured relatively easy with an inexpensive sensor and feature vectors can be derived with little effort. We achieve equal error rates in the order of 1.5{\%}-7{\%}, depending on the application device that is used to do the measurement.},
author = {Akkermans, A. H M and Kevenaar, T. A M and Schobben, D. W E},
booktitle = {Proceedings - Fourth IEEE Workshop on Automatic Identification Advanced Technologies, AUTO ID 2005},
doi = {10.1109/AUTOID.2005.11},
isbn = {0769524753},
issn = {03029743},
pages = {219--223},
title = {{Acoustic ear recognition for person identification}},
volume = {2005},
year = {2005}
}
@inproceedings{Ashby2011,
abstract = {A low-cost, consumer-grade, EEG-based individual authentication system is proposed in this work. While EEG signals are recorded, the subject performs four mental imagery tasks consisting of a baseline measurement, referential limb movement, counting, and rotation for 150 seconds each. The 150 seconds of data are divided into one second segments, from which features are obtained. Three sets of features are extracted from each electrode: 6th order autoregressive (AR) coefficients, power spectral density, and total power in five frequency bands. Two additional sets of features are extracted from interhemispheric data: interhemispheric power differences and interhemispheric linear complexity. These feature sets are combined into a feature vector that is then used by a linear support vector machine (SVM) with cross validation for classification. The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs). Using voting rules across groups of ten segments, we were able to achieve 100{\%} classification accuracy for each subject in each task. Though more work must be done with a larger subject pool as well as across multiple sessions, these results show that low-cost EEG authentication systems may be viable.},
author = {Ashby, Corey and Bhatia, Amit and Tenore, Francesco and Vogelstein, Jacob},
booktitle = {2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011},
doi = {10.1109/NER.2011.5910581},
isbn = {9781424441402},
issn = {1948-3546},
pages = {442--445},
title = {{Low-cost electroencephalogram (EEG) based authentication}},
year = {2011}
}
@article{Armstrong2015,
  title={Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics},
  author={Armstrong, Blair C and Ruiz-Blondet, Maria V and Khalifian, Negin and Kurtz, Kenneth J and Jin, Zhanpeng and Laszlo, Sarah},
  journal={Neurocomputing},
  volume={166},
  pages={59--67},
  year={2015},
  publisher={Elsevier}
}
@article{Bojinov2012,
abstract = {Cryptographic systems often rely on the secrecy of cryptographic keys given to users. Many schemes, however, cannot resist coercion attacks where the user is forcibly asked by an attacker to reveal the key. These attacks, known as rubber hose cryptanalysis, are often the easiest way to defeat cryptography. We present a defense against coercion attacks using the concept of implicit learning from cognitive psychology. Implicit learning refers to learning of patterns without any conscious knowledge of the learned pattern. We use a carefully crafted computer game to plant a secret password in the participant's brain without the participant having any conscious knowledge of the trained password. While the planted secret can be used for authentication, the participant cannot be coerced into revealing it since he or she has no conscious knowledge of it. We performed a number of user studies using Amazon's Mechanical Turk to verify that participants can successfully reauthenticate over time and that they are unable to reconstruct or even recognize short fragments of the planted secret.},
author = {Bojinov, Hristo (Stanford University) and Sanchez, Daniel (Northwestern University) and Reber, Paul (Northwestern University) and Boneh, Dan (Stanford University) and Lincoln, Patrick (Sri)},
doi = {10.1145/2594445},
isbn = {978-931971-95-9},
issn = {0001-0782},
journal = {Proceedings of the 21st USENIX conference on Security symposium},
pages = {1--13},
title = {{Neuroscience Meets Cryptography : Designing Crypto Primitives Secure Against Rubber Hose Attacks}},
year = {2012}
}
@inproceedings{braz2006,
  title={Security and usability: the case of the user authentication methods},
  author={Braz, Christina and Robert, Jean-Marc},
  booktitle={Proceedings of the 18th Conference on l'Interaction Homme-Machine},
  pages={199--203},
  year={2006},
  organization={ACM}
}
@article{Chen2016,
abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end- to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quan- tile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compres- sion and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.},
archivePrefix = {arXiv},
arxivId = {1603.02754},
author = {Chen, Tianqi and Guestrin, Carlos},
doi = {10.1145/2939672.2939785},
eprint = {1603.02754},
isbn = {9781450342322},
issn = {0146-4833},
journal = {arXiv},
keywords = {large-scale machine learning},
pages = {1--6},
title = {{XGBoost : Reliable Large-scale Tree Boosting System}},
year = {2016}
}
@inproceedings{Chen2015,
  title={Your song your way: Rhythm-based two-factor authentication for multi-touch mobile devices},
  author={Chen, Yimin and Sun, Jingchao and Zhang, Rui and Zhang, Yanchao},
  booktitle={Computer Communications (INFOCOM), 2015 IEEE Conference on},
  pages={2686--2694},
  year={2015},
  organization={IEEE}
}
@misc{Chuang2014,
abstract = {Two-step verification does not imply two-factor authentication. Conversely, two-factor authentication may not require two-step verification. With ubiquitous bio-sensors, we can strive for one-step two-factor authentication for wearable computing applications.},
author = {Chuang, John},
booktitle = {SOUPS},
keywords = {2FA,bio-sensory computing,multi-factor authentication,passthought,wearable authentication},
title = {{One-Step Two-Factor Authentication with Wearable Bio-Sensors}},
url = {https://cups.cs.cmu.edu/soups/2014/workshops/papers/biosensors{\_}chuang{\_}6.pdf},
year = {2014}
}
@incollection{Chuang2013b,
abstract = {With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each sub- ject, we can achieve 99{\%} authentication accuracy using single-channel EEG signals, which is on par with previous research employing multi- channel EEG signals using clinical-grade devices. In addition to the us- ability improvement oered by the single-channel dry-contact EEG sen- sor, we also study the usability of di erent classes of mental tasks. We nd that subjects have little diculty recalling chosen "pass-thoughts" (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived diculty and enjoyability of the tasks. These results can inform the design of authen- tication systems that guide users in choosing tasks that are both usable and secure.},
author = {Chuang, John and Nguyen, Hamilton and Wang, Charles and Johnson, Benjamin},
booktitle = {International Conference on Financial Cryptography and Data Security},
doi = {10.1007/978-3-642-41320-9_1},
file = {:Users/ffff/Library/Application Support/Mendeley Desktop/Downloaded/Chuang et al. - 2013 - I think, therefore I am Usability and security of authentication using brainwaves.pdf:pdf;:Users/ffff/Library/Application Support/Mendeley Desktop/Downloaded/Chuang et al. - 2013 - I think, therefore I am Usability and security of authentication using brainwaves.html:html},
isbn = {9783642413193},
issn = {03029743},
keywords = {Authentication,Computer Appl. in Administrative Data Processing,Data Encryption,EEG,Pass-thoughts,Systems and Data Security,Usability,authentication,e-Commerce/e-business,pass-thoughts,usability},
mendeley-tags = {Computer Appl. in Administrative Data Processing,Data Encryption,EEG,Systems and Data Security,authentication,e-Commerce/e-business,pass-thoughts,usability},
pages = {1--16},
shorttitle = {I Think, Therefore I Am},
title = {{I think, therefore I am: Usability and security of authentication using brainwaves}},
url = {http://link.springer.com/chapter/10.1007/978-3-642-41320-9{\_}1 http://link.springer.com/content/pdf/10.1007{\%}2F978-3-642-41320-9{\_}1.pdf},
year = {2013}
}
@inproceedings{curran2016passthoughts,
  title={Passthoughts authentication with low cost EarEEG},
  author={Curran, Max T and Yang, Jong-kai and Merrill, Nick and Chuang, John},
  booktitle={Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the},
  pages={1979--1982},
  year={2016},
  organization={IEEE}
}
@article{Frey2016,
abstract = {Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applica- tions, however the dissemination of the technology is limited, in part because of the price of the hardware. In this paper we compare side by side two EEG amplifiers, the consumer grade OpenBCI and the medical grade g.tec g.USBamp. For this purpose, we employed an original montage, based on the simultaneous recording of the same set of electrodes. Two set of recordings were performed. During the first experiment a simple adapter with a direct connection between the amplifiers and the electrodes was used. Then, in a second experiment, we attempted to discard any possible interference that one amplifier could cause to the other by adding “ideal” diodes to the adapter. Both spectral and temporal features were tested – the former with aworkload monitoring task, the latter with an visual P300 speller task. Overall, the results suggest that the OpenBCI board – or a similar solution based on the Texas Instrument ADS1299 chip – could be an effective alternative to traditional EEG devices. Even though a medical grade equipment still outperforms the OpenBCI, the latter gives very close EEG readings, resulting in practice in a classification accuracy that may be suitable for popularizing BCI uses.},
archivePrefix = {arXiv},
arxivId = {1606.02438},
author = {Frey, J{\'{e}}r{\'{e}}my},
doi = {10.5220/0005954501050114},
eprint = {1606.02438},
isbn = {9789897581977},
journal = {Proceedings of the 3rd International Conference on Physiological Computing Systems},
keywords = {amplifiers comparison,are promising communication devices,based on non-invasive neuroimaging,bci,between humans and machines,brain-computer interfaces,eeg,have many applica-,p300 speller,techniques such as electroencephalography,workload classification},
number = {PhyCS},
pages = {105--114},
title = {{Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications}},
year = {2016}
}
@article{Garrett2003a,
abstract = {The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.},
author = {Garrett, Deon and Peterson, David A. and Anderson, Charles W. and Thaut, Michael H.},
doi = {10.1109/TNSRE.2003.814441},
isbn = {1534-4320},
issn = {15344320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
keywords = {Brain-computer interface (BCI),Electroencephalogram (EEG),Feature selection,Genetic algorithms (GA),Neural networks,Pattern classification,Support vector machines (SVM)},
number = {2},
pages = {141--144},
pmid = {12899257},
title = {{Comparison of linear, nonlinear, and feature selection methods for EEG signal classification}},
volume = {11},
year = {2003}
}
@inproceedings{Holz2015,
  title={Biometric touch sensing: Seamlessly augmenting each touch with continuous authentication},
  author={Holz, Christian and Knaust, Marius},
  booktitle={Proceedings of the 28th Annual ACM Symposium on User Interface Software \& Technology},
  pages={303--312},
  year={2015},
  organization={ACM}
}
@article{Johnson2014,
abstract = {Authenticating users of computer systems based on their brainwave signals is now a realistic possibility, made possible by the increasing availability of EEG (electroencephalography) sensors in wireless headsets and wearable devices. This possibility is especially interesting because brainwave-based authentication naturally meets the criteria for two-factor authentication. To pass an authentication test using brainwave signals, a user must have both an inherence factor (his or her brain) and a knowledge factor (a chosen passthought). In this study, we investigate the extent to which both factors are truly necessary. In particular, we address the question of whether an attacker may gain advantage from information about a given target's secret thoughts.},
author = {Johnson, Benjamin and Maillart, Thomas and Chuang, John},
doi = {10.1145/2638728.2641710},
isbn = {9781450330473},
journal = {Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp '14 Adjunct},
pages = {1329--1338},
title = {{My thoughts are not your thoughts}},
url = {http://dl.acm.org/citation.cfm?doid=2638728.2641710},
year = {2014}
}
@article{Kidmose2013b,
abstract = {The use of brain monitoring based on EEG, in natural environments and over long time periods, is hindered by the limited portability of current wearable systems, and the invasiveness of implanted systems. To that end, we introduce an ear-EEG recording device based on generic earpieces which meets key patient needs (discreet, unobstrusive, user-friendly, robust) and that is low-cost and suitable for off-the-shelf use; thus promising great advantages for healthcare applications. Its feasibility is validated in a comprehensive comparative study with our established prototype, based on a personalized earpiece, for a key EEG paradigm.},
author = {Kidmose, P. and Looney, D. and Jochumsen, L. and Mandic, D. P.},
doi = {10.1109/EMBC.2013.6609557},
isbn = {9781457702167},
issn = {1557170X},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
pages = {543--546},
pmid = {24109744},
title = {{Ear-EEG from generic earpieces: a feasibility study}},
volume = {2013},
year = {2013}
}
@article{Kidmose2013a,
abstract = {A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over steady-state and transient paradigms, and across a population of subjects. The respective steady-state responses are evaluated in terms of signal-to-noise ratio and statistical significance, while the qualitative analysis of the transient responses is performed by considering grand averaged event-related potential (ERP) waveforms. The outcomes of this study demonstrate conclusively that the ear-EEG signals, in terms of the signal-to-noise ratio, are on par with conventional EEG recorded from electrodes placed over the temporal region.},
author = {Kidmose, Preben and Looney, David and Ungstrup, Michael and Rank, Mike Lind and Mandic, Danilo P.},
doi = {10.1109/TBME.2013.2264956},
isbn = {0018-9294},
issn = {00189294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Auditory steady-state response (ASSR),ear-EEG,event-related potentials,evoked potentials (EP),steady-state visual evoked potentials (SSVEP)},
number = {10},
pages = {2824--2830},
pmid = {23722447},
title = {{A study of evoked potentials from ear-EEG}},
volume = {60},
year = {2013}
}
@inproceedings{Looney2011,
abstract = {We introduce a novel approach to brain monitoring based on electroencephalogram (EEG) recordings from within the ear canal. While existing clinical and wearable systems are limited in terms of portability and ease of use, the proposed in-the-ear (ITE) recording platform promises a number of advantages including ease of implementation, minimally intrusive electrodes and enhanced accuracy (fixed electrode positions). It thus facilitates a crucial step towards the design of brain computer interfaces that integrate naturally with daily life. The feasibility of the ITE concept is demonstrated with recordings made from electrodes embedded on an earplug which are benchmarked against conventional scalp electrodes for a classic EEG paradigm.},
author = {Looney, D. and Park, C. and Kidmose, P. and Rank, M. L. and Ungstrup, M. and Rosenkranz, K. and Mandic, D. P.},
booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
doi = {10.1109/IEMBS.2011.6091733},
isbn = {9781424441211},
issn = {1557170X},
pages = {6882--6885},
pmid = {22255920},
title = {{An in-the-ear platform for recording electroencephalogram}},
year = {2011}
}
@article{Looney2012a,
abstract = {The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and user-centered approach to brain monitoring. The in-the-ear EEG (Ear-EEG) recording concept is tested using several standard EEG paradigms, benchmarked against standard onscalp EEG, and its feasibility proven. Such a system promises a number of advantages, including fixed electrode positions, user comfort, robustness to electromagnetic interference, feedback to the user, and ease of use. The Ear-EEG platform could also support additional biosensors, extending its reach beyond EEG to provide a powerful health-monitoring system for those applications that require long recording periods in a natural environment.},
author = {Looney, David and Kidmose, Preben and Park, Cheolsoo and Ungstrup, Michael and Rank, Mike and Rosenkranz, Karin and Mandic, Danilo},
doi = {10.1109/MPUL.2012.2216717},
isbn = {2154-2287},
issn = {21542287},
journal = {IEEE Pulse},
number = {6},
pages = {32--42},
pmid = {23247157},
title = {{The in-the-ear recording concept: User-centered and wearable brain monitoring}},
volume = {3},
year = {2012}
}
@inproceedings{Lu2014,
  title={Unobtrusive gait verification for mobile phones},
  author={Lu, Hong and Huang, Jonathan and Saha, Tanwistha and Nachman, Lama},
  booktitle={Proceedings of the 2014 ACM international symposium on wearable computers},
  pages={91--98},
  year={2014},
  organization={ACM}
}
@article{Maiorana2016,
  title={On the permanence of EEG signals for biometric recognition},
  author={Maiorana, Emanuele and La Rocca, Daria and Campisi, Patrizio},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={11},
  number={1},
  pages={163--175},
  year={2016},
  publisher={IEEE}
}
@article{Marcel2007a,
abstract = {In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.},
author = {Marcel, S{\'{e}}batien and Millan, Jos{\'{e}} del R},
doi = {10.1109/TPAMI.2007.1012},
isbn = {0162-8828 (Print)},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Biometry,Electroencephalogram,Emerging technologies,Machine learning,Probabilistic algorithms,Signal processing},
number = {4},
pages = {743--748},
pmid = {17299229},
title = {{Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation}},
volume = {29},
year = {2007}
}
@inproceedings{Martinovic2012,
abstract = {Brain computer interfaces (BCI) are becoming in- creasingly popular in the gaming and entertainment in- dustries. Consumer-grade BCI devices are available for a few hundred dollars and are used in a variety of appli- cations, such as video games, hands-free keyboards, or as an assistant in relaxation training. There are application stores similar to the ones used for smart phones, where application developers have access to an API to collect data from the BCI devices. The security risks involved in using consumer-grade BCI devices have never been studied and the impact of malicious software with access to the device is unex- plored. We take a first step in studying the security impli- cations of such devices and demonstrate that this upcom- ing technology could be turned against users to reveal their private and secret information. We use inexpensive electroencephalography (EEG) based BCI devices to test the feasibility of simple, yet effective, attacks. The cap- tured EEG signal could reveal the users private informa- tion about, e.g., bank cards, PIN numbers, area of living, the knowledge of the known persons. This is the first attempt to study the security implications of consumer- grade BCI devices. We show that the entropy of the pri- vate information is decreased on the average by approx- imately 15{\%} - 40{\%} compared to random guessing at- tacks.},
address = {Berkeley, CA, USA},
annote = {definitely something to be said for probabilistically gauging a user's familiarity with a given stimulus!!!!! very clever attack will be relevant in the future!!!!!!},
author = {Martinovic, Ivan and Davies, Doug and Frank, Mario and Perito, Daniele and Ros, Tomas and Song, Dawn},
booktitle = {Usenixorg},
isbn = {978-931971-95-9},
issn = {0733-8716},
keywords = {Folder - bmi - noninvasive EEG - oblique uses for},
mendeley-tags = {Folder - bmi - noninvasive EEG - oblique uses for},
pages = {1--16},
publisher = {USENIX Association},
series = {Security'12},
title = {{On the Feasibility of Side-Channel Attacks with Brain-Computer Interfaces}},
url = {https://www.usenix.org/system/files/conference/usenixsecurity12/sec12-final56.pdf},
year = {2012}
}
@article{michalska2009openbci,
  title={OpenBCI: Framework for Brain-Computer Interfaces},
  author={Michalska, Magdalena},
  journal={University of Warsaw Faculty of Mathematics, Informatics and Mechanics},
  year={2009}
}
@article{Mikkelsen2015,
abstract = {A method for measuring electroencephalograms (EEG) from the outer ear, so-called ear-EEG, has recently been proposed. The method could potentially enable robust recording of EEG in natural environments. The objective of this study was to substantiate the ear-EEG method by using a larger population of subjects and several paradigms. For rigor, we considered simultaneous scalp and ear-EEG recordings with common reference. More precisely, 32 conventional scalp electrodes and 12 ear electrodes allowed a thorough comparison between conventional and ear electrodes, testing several different placements of references. The paradigms probed auditory onset response, mismatch negativity, auditory steady-state response and alpha power attenuation. By comparing event related potential (ERP) waveforms from the mismatch response paradigm, the signal measured from the ear electrodes was found to reflect the same cortical activity as that from nearby scalp electrodes. It was also found that referencing the ear-EEG electrodes to another within-ear electrode affects the time-domain recorded waveform (relative to scalp recordings), but not the timing of individual components. It was furthermore found that auditory steady-state responses and alpha-band modulation were measured reliably with the ear-EEG modality. Finally, our findings showed that the auditory mismatch response was difficult to monitor with the ear-EEG. We conclude that ear-EEG yields similar performance as conventional EEG for spectrogram-based analysis, similar timing of ERP components, and equal signal strength for sources close to the ear. Ear-EEG can reliably measure activity from regions of the cortex which are located close to the ears, especially in paradigms employing frequency-domain analyses.},
author = {Mikkelsen, Kaare B. and Kappel, Simon L. and Mandic, Danilo P. and Kidmose, Preben},
doi = {10.3389/fnins.2015.00438},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Alpha band power,Auditory evoked potentials,Auditory steady-state response,Ear-EEG,Mobile EEG},
number = {NOV},
pmid = {26635514},
title = {{EEG recorded from the ear: Characterizing the Ear-EEG Method}},
volume = {9},
year = {2015}
}
@article{Monrose1997,
abstract = {In an effort to confront the challenges brought forward by the networking revolution of the past few years, we present improved techniques for authorized access to computer system resources and data. More than ever before, the Internet is changing computing as we know it. The possibilities of this global network seem limitless; unfortunately, with this global access comes increased chances of malicious attack and intrusion. Alternatives to traditional access control measures are in high demand. In what follows we present one such alternative: computer access via keystroke dynamics. A database of 42 profiles was constructed based on keystroke patterns gathered from various users performing structured and unstructured tasks. We study the performance of a system for recognition of these users, and present a toolkit for analyzing system performance under varying criteria.},
author = {Monrose, F. and Rubin, a.},
doi = {10.1145/266420.266434},
isbn = {0897919122},
journal = {Proc. of the 4th ACM Conf. on Computer and Communications Security},
keywords = {Biometrics,computer security,keystroke dynamics,pattern,recognition},
pages = {48--56},
title = {{Authentication via keystroke dynamics}},
year = {1997}
}
@article{Monrose2000,
abstract = {More than ever before the Internet is changing computing as we know it. Global access to information and resources is becoming an integral part of nearly every aspect of our lives. Unfortunately, with this global network access comes increased chances of malicious attack and intrusion. In an effort to confront the new threats unveiled by the networking revolution of the past few years reliable, rapid, and unintrusive means for automatically recognizing the identity of individuals are now being sought. In this paper we examine an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type.},
author = {Monrose, Fabian and Rubin, Aviel D.},
doi = {10.1016/S0167-739X(99)00059-X},
issn = {0167739X},
journal = {Future Generation Computer Systems},
number = {4},
pages = {351--359},
title = {{Keystroke dynamics as a biometric for authentication}},
volume = {16},
year = {2000}
}
@inproceedings{Genaro2014,
  title={Understanding the wearability of head-mounted devices from a human-centered perspective},
  author={Genaro Motti, Vivian and Caine, Kelly},
  booktitle={Proceedings of the 2014 ACM International Symposium on Wearable Computers},
  pages={83--86},
  year={2014},
  organization={ACM}
}
@misc{Nymi,
author = {Nymi},
title = {{Nymi Band - Always-On Authentication}},
url = {https://nymi.com},
urldate = {2017-02-07}
}
@article{Palaniappan2008,
abstract = {Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.},
author = {Palaniappan, Ramaswamy},
doi = {10.1142/S0129065708001373},
isbn = {0129-0657 (Print)},
issn = {0129-0657},
journal = {International journal of neural systems},
keywords = {authentication,biometric,electroencephalogram,thought activities},
number = {1},
pages = {59--66},
pmid = {18344223},
title = {{Two-stage biometric authentication method using thought activity brain waves.}},
volume = {18},
year = {2008}
}
@inproceedings{palaniappan2006electroencephalogram,
  title={Electroencephalogram signals from imagined activities: A novel biometric identifier for a small population},
  author={Palaniappan, Ramaswamy},
  booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
  pages={604--611},
  year={2006},
  organization={Springer}
}
@article{Garrett2003a,
abstract = {The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.},
author = {Garrett, Deon and Peterson, David A. and Anderson, Charles W. and Thaut, Michael H.},
doi = {10.1109/TNSRE.2003.814441},
isbn = {1534-4320},
issn = {15344320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
keywords = {Brain-computer interface (BCI),Electroencephalogram (EEG),Feature selection,Genetic algorithms (GA),Neural networks,Pattern classification,Support vector machines (SVM)},
number = {2},
pages = {141--144},
pmid = {12899257},
title = {{Comparison of linear, nonlinear, and feature selection methods for EEG signal classification}},
volume = {11},
year = {2003}
}
@article{peirce2007psychopy,
  title={PsychoPy-psychophysics software in Python},
  author={Peirce, Jonathan W},
  journal={Journal of neuroscience methods},
  volume={162},
  number={1},
  pages={8--13},
  year={2007},
  publisher={Elsevier}
}
@article{Poulos2002,
abstract = {OBJECTIVES: This paper focusses on the person identification problem based on features extracted from the ElectroEncephaloGram (EEG). A bilinear rather than a purely linear model is fitted on the EEG signal, prompted by the existence of non-linear components in the EEG signal--a conjecture already investigated in previous research works. The novelty of the present work lies in the comparison between the linear and the bilinear results, obtained from real field EEG data, aiming towards identification of healthy subjects rather than classification of pathological cases for diagnosis. METHODS: The EEG signal of a, in principle, healthy individual is processed via (non)linear (AR, bilinear) methods and classified by an artificial neural network classifier. RESULTS: Experiments performed on real field data show that utilization of the bilinear model parameters as features improves correct classification scores at the cost of increased complexity and computations. Results are seen to be statistically significant at the 99.5{\%} level of significance, via the chi 2 test for contingency. CONCLUSIONS: The results obtained in the present study further corroborate existing research, which shows evidence that the EEG carries individual-specific information, and that it can be successfully exploited for purposes of person identification and authentication.},
author = {Poulos, M and Rangoussi, M and Alexandris, N and Evangelou, a},
isbn = {0026-1270 (Print)},
issn = {0026-1270},
journal = {Methods of information in medicine},
keywords = {bilinear model,eeg,lvq neural,non-linear processing,person identification},
number = {1},
pages = {64--75},
pmid = {11933767},
title = {{Person identification from the EEG using nonlinear signal classification.}},
volume = {41},
year = {2002}
}
@inproceedings{Rogers2015,
  title={An approach for user identification for head-mounted displays},
  author={Rogers, Cynthia E and Witt, Alexander W and Solomon, Alexander D and Venkatasubramanian, Krishna K},
  booktitle={Proceedings of the 2015 ACM International Symposium on Wearable Computers},
  pages={143--146},
  year={2015},
  organization={ACM}
}
@article{Ruiz2017,
  title={Permanence of the CEREBRE brain biometric protocol},
  author={Ruiz-Blondet, Maria V and Jin, Zhanpeng and Laszlo, Sarah},
  journal={Pattern Recognition Letters},
  volume={95},
  pages={37--43},
  year={2017},
  publisher={Elsevier}
}
@article{sasse2001,
  title={Transforming the ‘weakest link’—a human/computer interaction approach to usable and effective security},
  author={Sasse, Martina Angela and Brostoff, Sacha and Weirich, Dirk},
  journal={BT technology journal},
  volume={19},
  number={3},
  pages={122--131},
  year={2001},
  publisher={Springer}
}
@misc{Spielberg2002,
author = {Spielberg, Steven},
publisher = {Twentieth Century Fox},
title = {{Minority Report}},
year = {2002}
}
@article{Strickland2016,
abstract = {Brain hacking gadgets could soon be an unobtrusive part of daily life, thanks to EEG sensors that fit snugly inside the ear. Two research groups are making progress on discreet devices that offer reliable brain data—and that reliability is a key point. A few neuro gadgets for consumers have already hit the market, but it's not at all clear that they deliver the promised brain data.},
author = {Strickland, Eliza},
journal = {IEEE Spectrum},
month = {jul},
title = {{In-Ear EEG Makes Unobtrusive Brain-Hacking Gadgets a Real Possibility}},
url = {http://spectrum.ieee.org/the-human-os/biomedical/devices/in-ear-eeg-makes-unobtrusive-brain-hacking-gadgets-a-real-possibility},
year = {2016}
}
@inproceedings{Tartz2015,
  title={Hand Biometrics Using Capacitive Touchscreens},
  author={Tartz, Robert and Gooding, Ted},
  booktitle={Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software \& Technology},
  pages={67--68},
  year={2015},
  organization={ACM}
}
@article{Thorpe2005,
abstract = {We present a novel idea for user authentication that we call pass-thoughts. Recent advances in Brain-Computer Interface (BCI) technology indicate that there is potential for a new type of human-computer interaction: a user transmitting thoughts directly to a computer. The goal of a pass-thought system would be to extract as much entropy as possible from a user's brain signals upon "transmitting" a thought. Provided that these brain signals can be recorded and processed in an accurate and repeatable way, a pass-thought system might provide a quasi two-factor, changeable, authentication method resistant to shoulder-surfing. The potential size of the space of a pass-thought system would seem to be unbounded in theory, although in practice it will be finite due to system constraints. In this paper, we discuss the motivation and potential of pass-thought authentication, the status quo of BCI technology, and outline the design of what we believe to be a currently feasible pass-thought system. We also briefly mention the need for general exploration and open debate regarding ethical considerations for such technologies.},
author = {Thorpe, Julie and {Van Oorschot}, P C and Somayaji, Anil},
doi = {10.1145/1146269.1146282},
isbn = {1595933174},
journal = {Proceedings of the 2005 workshop on New security paradigms},
pages = {45--56},
title = {{Pass-thoughts: authenticating with our minds}},
url = {http://doi.acm.org/10.1145/1146269.1146282},
year = {2005}
}
@misc{UnitedSciences,
author = {{United Sciences}, LLC.},
title = {{Aware Hearables - World's First Custom-Fit Bluetooth Headphones with Brain and Body Sensors}},
url = {http://efitaware.com},
urldate = {2017-02-07}
}
@misc{voix2015settable,
  title={Settable compound delivery device and system for inflatable in-ear device},
  author={Voix, J{\'e}r{\'e}mie and Maloney, Michael and Turcot, Michael C},
  year={2015},
  month=aug # "~18",
  publisher={Google Patents},
  note={US Patent 9,107,772}
}