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We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model.-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form is based on the point estimates of the previous outputs. In this(More)
In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus(More)
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with(More)
—Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. Ideally, it is desirable that a dynamic Takagi-Sugeno fuzzy model should give accurate global non-linear prediction and at the same time that its local models are close approximations to the local linearizations of the(More)
Shoogle is a novel, intuitive interface for sensing data withina mobile device, such as presence and properties of textmessages or remaining resources. It is based around activeexploration: devices are shaken, revealing the contents rattlingaround "inside". Vibrotactile display and realistic impactsonification create a compelling system. Inertial sensingis(More)
Designing user interfaces which can cope with unconventional control properties is challenging , and conventional interface design techniques are of little help. This paper examines how interactions can be designed to explicitly take into account the uncertainty and dynamics of control inputs. In particular, the asymmetry of feedback and control channels is(More)
As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields. However, two major problems arise when the model is applied to a large data-set with repeated measurements. One stems from the systematic(More)
The area of multimodal interaction has expanded rapidly. However, the implementation of multimodal systems still remains a difficult task. Addressing this problem, we describe the OpenInterface (OI) framework, a component-based tool for rapidly developing multimodal input interfaces. The OI underlying conceptual component model includes both generic and(More)
— Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian(More)