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Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper explores different approaches and configurations for classifying Mental Workload (MWL) from a continuous HCI task, to identify and understand potential limitations and data processing decisions. In particular, we investigate three overall approaches: a logistic regression method, a supervised shallow method (SVM), and a supervised deep learning method (CNN). We examine personalised and generalised models, as well as consider different features and ways of labelling the data. Our initial explorations show that generalised models can perform as well as personalised ones and that deep learning can be a suitable approach for medium size datasets. To provide additional practical advice for future brain-computer interaction systems, we conclude by discussing the limitations and data-preparation needs of different machine learning approaches. We also make recommendations for avenues of future work that are most promising for the machine learning of fNIRS data.

In the overlap between Human-Computer Interaction (HCI) and Cinematics, sits an interest in physiological responses to experiences. Focusing particularly on brain data, Neurocinematics has emerged as a research field using Brain-Computer Interface (BCI) sensors. Where previous work found inter subject correlations (ISC) between brain measurements of people watching movies in constrained conditions using functional magnetic resonance imaging (fMRI), we seek to examine similar responses in more naturalistic settings using functional Near Infrared Spectroscopy (fNIRS). fNIRS has been shown to be highly suitable for HCI studies, being more portable than fMRI and more tolerant of many natural movements than Electroencephalography (EEG). Early results found significant ISC, which gives a lot of hope and potential for using fNIRS in Neurocinematics.

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.

Recent work has demonstrated that functional Near-Infrared Spectroscopy has the potential to measure changes in Mental Workload with increasing ecological validity. It is not clear, however, whether these measurements are affected by anxiety and stress of the workload, where our informal observations see some participants enjoying the workload and succeeding in tasks, while others worry and struggle with the tasks. This research evaluated the effects of stress on fNIRS measurements and performance, using the Montreal Imaging Stress Task to manipulate the experience of stress. While our results largely support this hypothesis, our conclusions were undermined by data from the Rest condition, which indicated that Mental Workload and Stress were often higher than during tasks. We hypothesize that participants were experiencing anxiety in anticipation of subsequent stress tasks. We discuss this hypothesis and present a revised study designed to better control for this result.

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.

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.