Nick Merrill

Habits of the Heart(rate): Social Interpretation of Biosignals in Two Interaction Contexts

We investigate interpretations of a biosignal (heartrate) in uncertain social interactions. We describe the quantitative and qualitative results of a randomized vignette experiment in which subjects were asked to make assessments about an acquaintance based on an imagined scenario that included shared heartrate information. We compare the results of this experiment in adversarial and non-adversarial contexts of interaction. We find that elevated heartrate transmits cues about mood in both contexts, but that these cues do not appear to impact assessments of trustworthiness, reliability and dependability. Counter to our expectations, we find that normal (rather than elevated) heartrate leads to negative trust-related assessments, but only in an adversarial context. Our qualitative analysis points to the role of social expectations in shaping contextual interpretations of heartrate, and reveals individual differences in the way interpretations are constructed. We unpack some of the ways that social meanings can arise from biosensor data, and discuss considerations for those designing interactions with wearables.

Passthoughts authentication with low cost EarEEG

Personal and wearable computing are moving toward smaller and more seamless devices. We explore how this trend could be mirrored in an authentication scheme based on electroencephalography (EEG) signals collected from the ear. We evaluate this model using a low cost, single-channel, consumer grade device for data collection. Using data from 12 study participants who performed a set of 5 mental tasks, we achieve a 44% reduction in half total error rate (HTER) compared with a random classifier, corresponding to a 72% authentication accuracy in within-participants analyses and a 60% reduction and 80% accuracy in between-participant analyses. Given our results and those of previous research, we conclude that earEEG shows potential as a uniquely convenient authentication method as it is integrable into devices like earbud headphones already commonly worn in the ear, and the mental gestures generating the signal are invisible to would-be eavesdroppers.Personal and wearable computing are moving toward smaller and more seamless devices. We explore how this trend could be mirrored in an authentication scheme based on electroencephalography (EEG) signals collected from the ear. We evaluate this model using a low cost, single-channel, consumer grade device for data collection. Using data from 12 study participants who performed a set of 5 mental tasks, we achieve a 44% reduction in half total error rate (HTER) compared with a random classifier, corresponding to a 72% authentication accuracy in within-participants analyses and a 60% reduction and 80% accuracy in between-participant analyses. Given our results and those of previous research, we conclude that earEEG shows potential as a uniquely convenient authentication method as it is integrable into devices like earbud headphones already commonly worn in the ear, and the mental gestures generating the signal are invisible to would-be eavesdroppers.

Classifying mental gestures with in-ear EEG

While brain-computer interfaces (BCI) based on electroencephalography (EEG) have improved dramatically over the past five years, their inconvenient, head-worn form factor has challenged their wider adoption. In this paper, we investigate how EEG signals collected from the ear could be used for “gestural” control of a brain-computer interface (BCI). Specifically, we investigate the efficacy of a support vector classifier (SVC) in distinguishing between mental tasks, or gestures, recorded by a modified, consumer headset. We find that an SVC reaches acceptable BCI accuracy for nine of the subjects in our pool (n=12), and distinguishes at least one pair of gestures better than chance for all subjects. User surveys highlight the need for longer-term research on user attitudes toward in-ear EEG devices, for discreet, non-invasive BCIs.

Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique

This paper exhibits two methods for decreasing the time associated with training a machine learning classifier on biometric signals. Using electroencephalography (EEG) data obtained from a consumer-grade headset with a single electrode, we show that these methods produce significant gains in the computational performance and calibration time of a simple brain-computer interface (BCI) without significantly decreasing accuracy. We discuss the relevance of reduced feature vector size to the design of physiological computing applications.

What's It Mean to "Be Social" in VR?: Mapping the Social VR Design Ecology

The emerging ecology of commercial social VR currently includes a diverse set of applications and competing models of what it means to be social in VR. This study maps a slice of this ecology, comparing and contrasting ways different applications frame, support, shape, or constrain social interaction. We deploy a method of design-oriented autobiographical landscape research to examine five platforms: Facebook Spaces, Rec Room, High Fidelity, VRChat, and AltspaceVR. We analyze design choices underlying these environments and draw attention to issues of space and place, locomotion, and social mechanics. Drawing on this analysis, we identify key issues and concerns for future research and design in social VR.

An Interface without A User: An Exploratory Design Study of Online Privacy Policies and Digital Legalese

Privacy policies are critical to understanding ones rights on online platforms, yet few users read them. In this pictorial, we approach this as a systemic issue that is part a failure of interaction design. We provided a variety of people with printed packets of privacy policies, aiming to tease out this forms capabilities and limitations as a design interface, to understand peoples perception and uses, and to critically imagine pragmatic revisions and creative alternatives to existing privacy policies.

Exploring the Feasibility and Performance of One-step Three-factor Authentication with Ear-EEG.

Multi-factor authentication presents a robust method to secure our private information, but typically requires multiple actions by the user resulting in a high cost to usability and limiting adoption. A usable system should also be unobtrusive and inconspicuous. We present and discuss a system with the potential to engage all three factors of authentication (inherence, knowledge, and possession) in a single step using an earpiece that implements brain-based authentication using electroencephalography (EEG). We demonstrate its potential by collecting EEG data using manufactured custom-fit earpieces with embedded electrodes and testing a variety of authentication scenarios. Across all participants’ best-performing “passthoughts”, we are able to achieve 0% false acceptance and 0.36% false rejection rates, for an overall accuracy of 99.82%, using one earpiece with three electrodes. Furthermore, we find no successful attempts simulating impersonation attacks. We also report on perspectives from our participants. Our results suggest that a relatively inexpensive system using a single electrode-laden earpiece could provide a discreet, convenient, and robust method for one-step multi-factor authentication.

Interrogating Biosensing in Everyday Life

This workshop seeks to expand our understanding and imaginations regarding the possible roles biosensors (sensors measuring humans) can-and should-play in everyday life. By applying a critical lens to issues of interpretation, representation, and experience around biosensing and biosensors, we aim to shape research agendas within DIS, and generate new recommendations for designers working with biosensors or their data.