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The motivation behind using physiological measures to estimate cognitive activity is typically to build technology that can help people to understand themselves and their work, or indeed for systems to do so and adapt. While functional Near Infrared Spectroscopy (fNIRS) has been shown to reliably reflect manipulations of mental workload in different work tasks, we still need to establish whether fNIRS can differentiate variety within common office-like tasks in order to broaden our understanding of the factors involved in tracking them in real working conditions. 20 healthy participants (8 females, 12 males), whose work included office-like tasks, took part in a user study that investigated a) the sensitivity of fNIRS for measuring mental workload variations in representations of everyday reading and writing tasks, and b) how representations of natural interruptions are reflected in the data. Results supported fNIRS measuring PFC activation in differentiating between workload levels for reading tasks but not writing tasks in terms of increased oxygenated haemoglobin (O2Hb) and decreased deoxygenated haemoglobin (HHb), for harder conditions compared to easier conditions. There was considerable support for fNIRS in detecting changes in workload levels due to interruptions. Variations in workload levels during the interruptions could be understood in relation to spare capacity models. These findings may guide future work into sustained monitoring of cognitive activity in real-world settings.

We present the first stage of our on-going artist-driven BCI collaboration, where we equipped an artist with the brain scanning technique functional Near Infrared Spectroscopy (fNIRS) in order to record mental workload levels during her creative practice. The artists are interested in exposing the hidden cognitive processes involved in their creative practice, in order to reuse or integrate the data into their performances. The researchers are interested in collecting unstructured ‘in the wild’ fNIRS data, and to see how the artists interpret the data retrospectively. We highlight some interesting early examples from the data and describe our on-going plans. We will have completed a second data collection before the workshop.

As technology pervades our everyday life, tasks are increasingly “dominated by mental rather than physical task components.” Moreover, with the recent advancements in technology, the human role has moved towards a supervisory and decision-making one, this further increasing the demands on our limited mental resources. It is, therefore, crucially important to consider, measure, and evaluate human limitations in terms of their cognition.

An extensible, decentralized network of self-governing objects connected to a shared but variable power supply is a realistic problem domain for certain demand-management problems arising in the context of futuristic systems connected to smart power grids. An algorithmic framework of such a network is presented and discussed in this paper. Each object of the network has a power demand and a priority, and it is interconnected and able to exchange information with all other objects in the system. As each object shares information (its power demand and priority) with the other objects, the system as a whole exhibits self-governance, and is able to be managed without a centralized controller. Our model, which we term as decentralized power distribution (DPD) for such a network, also allows us to formulate algorithms for distributing available power among objects, along with analyses of correctness and performance.

Recent efforts have shown that functional near-infrared spectroscopy (fNIRS) has potential value for brain sensing in HCI user studies. Research has shown that, although large head movement significantly affects fNIRS data, typical keyboard use, mouse movement, and non-task-related verbalisations do not affect measurements during Verbal tasks. This work aims to examine the Reliability of fNIRS, by 1) confirming these prior findings, and 2) significantly extending our understanding of how artefacts affect recordings during Spatial tasks, since much of user interfaces and interaction is inherently spatial. Our results show that artefacts have a significantly different impact during Verbal and Spatial tasks. We contribute clearer insights into using fNIRS as a tool within HCI user studies.

An extensible, decentralized Internet of Things (IoT), with self-governing objects connected to a shared, variable power supply, is a realistic problem domain for certain demand-management problems arising in the context of future “smart homes” connected to smart power grids. A theoretical framework of such an IoT is presented and discussed in this paper. Each object of the IoT has a power demand and a priority, and it is interconnected and able to exchange information with all other objects in the system. As each object shares information (its power demand and priority) with the other objects, the system as a whole gains self-governance, or is able to be managed without a centralized controller. Our model of such an IoT also gives four applicable algorithms describing the behavior of the objects, along with analyses of correctness and performance.

The Think Aloud Protocol (TAP) is a verbalisation technique widely employed in HCI user studies to give insight into user experience, yet little work has explored the impact that TAPs have on participants during user studies. This paper utilises a brain sensing technique, fNIRS, to observe the effect that TAPs have on participants. Functional Near-Infrared Spectroscopy (fNIRS) is a brain sensing technology that offers the potential to provide continuous, detailed insight into brain activity, enabling an objective view of cognitive processes during complex tasks. Participants were asked to perform a mathematical task under 4 conditions: nonsense verbalisations, passive concurrent think aloud protocol, invasive concurrent think aloud protocol, and a baseline of silence. Subjective ratings and performance measures were collected during the study. Our results provide a novel view into the effect that different forms of verbalisation have on workload during tasks. Further, the results provide a means for estimating the effect of spoken artefacts when measuring workload, which is another step towards our goal of proactively involving fNIRS analysis in ecologically valid user studies.