Browsing by Author "Hietala, Paavo"
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Item Free-Form Gaze Passwords from Cameras Embedded in Smart Glasses(2019-12) Friström, Eira; Lius, Elias; Ulmanen, Niki; Hietala, Paavo; Kärkkäinen, Pauliina; Mäkinen, Tommi; Sigg, Stephan; Findling, Rainhard; Department of Computer Science; Department of Neuroscience and Biomedical Engineering; Department of Communications and Networking; Ambient Intelligence; Department of Computer Science; Department of Neuroscience and Biomedical Engineering; Aalto UniversityContemporary personal mobile devices support a variety of authentication approaches, featuring different levels of security and usability. With cameras embedded in smart glasses, seamless, hands-free mobile authentication based on gaze is possible. Gaze authentication relies on knowledge as a secret, and gaze passwords are composed from a series of gaze points or gaze gestures. This paper investigates the concept of free-form mobile gaze passwords. Instead of relying on gaze gestures or points, free-form gaze gestures exploit the trajectory of the gaze over time. We collect and investigate a set of 29 different free-form gaze passwords from 19 subjects. In addition, the practical security of the approach is investigated in a study with 6 attackers observing eye movements during password input to subsequently perform spoofing. Our investigation indicates that most free-form gaze passwords can be expressed as a set of common geometrical shapes. Further, our free-form gaze authentication yields a true positive rate of 81% and a false positive rate with other gaze passwords of 12%, while targeted observation and spoofing is successful in 17.5% of all cases. Our usability study reveals that further work on the usability of gaze input is required as subjects reported that they felt uncomfortable creating and performing free-form passwords.Item Improving MEG source estimation with joint analysis of multiple subjects(2021-10-18) Hietala, Paavo; Henriksson, Linda; Perustieteiden korkeakoulu; Parkkonen, LauriMagnetoencephalography (MEG) is a noninvasive brain imaging method which measures the magnetic field generated by neural currents. Interpretation of cortical activation patterns from the sensor data is an ill-posed inverse problem and a central challenge in MEG data analysis. Commonly used solutions for the inverse problem operate on a per subject basis, and group analysis is done separately on the individual results. A number of multi-subject joint analysis methods have been proposed, utilizing the additional information from multiple subjects in source estimation. The aim of this thesis is to explore the available multi-subject approaches and assess the performance of a selection of methods in a retinotopic mapping task. All brains are structurally unique, which could be exploited in improving the spatial accuracy of MEG. The effects have been demonstrated with a handful of multi-subject methods in both simulations and real measurements. The options range from averaging individual source estimates to solving all inverse problems simultaneously as coupled regression problems. Methods incorporating group data in the inverse solutions are mainly based on either group optimization of Bayesian hyperparameters or multi-task learning. Three multi-subject methods were compared: eLORETA with source-space averaging, minimum Wasserstein estimates (MWE) and MWE with source-space averaging. The dataset consisted of retinotopic mapping measurements from 20 subjects. The results were quantified by measuring the geodesic distance between 60--100 ms peak activations and the primary visual cortex (V1). All three methods show an improvement of 4--7 mm compared to individual median distances of 30--36 mm when at least 10 subjects are considered simultaneously. Additionally, the peak activation locations comply better with established retinotopic maps of V1 when the subject count is increased. Averaged eLORETA outperformed MWE with all subject counts. These results mostly conform to previous studies and suggest that higher spatial accuracy can be achieved with multi-subject analysis of MEG data. Further comparison of multi-subject methods with a selection of different cognitive tasks is recommended.Item Improving source estimation of retinotopic MEG responses by combining data from multiple subjects(MIT Press, 2024-08-12) Hietala, Paavo; Kurki, Ilmari; Hyvärinen, Aapo; Parkkonen, Lauri; Henriksson, Linda; Department of Neuroscience and Biomedical Engineering; University of HelsinkiMagnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.Item Reaaliaikaisen spektrin estimointimenetelmän implementointi OPM-MEG-laitteistoon(2018-12-16) Hietala, Paavo; Iivanainen, Joonas; Sähkötekniikan korkeakoulu; Turunen, Markus
