Class discovery from semi-structured EEG data for affective computing and personalisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the inter-relationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the ground- truth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome

Fast fringe pattern phase demodulation using FIR Hilbert transformers

This paper suggests the use of FIR Hilbert transformers to extract the phase of fringe patterns. This method is computationally faster than any known spatial method that produces wrapped phase maps. Also, the algorithm does not require any parameters to be adjusted which are dependent upon the specific fringe pattern that is being processed, or upon the particular setup of the optical fringe projection system that is being used. It is therefore particularly suitable for full algorithmic automation. The accuracy and validity of the suggested method has been tested using both computer-generated and real fringe patterns. This novel algorithm has been proposed for its advantages in terms of computational processing speed as it is the fastest available method to extract the wrapped phase information from a fringe pattern

Shifting of wrapped phase maps in the frequency domain using a rational number

The number of phase wraps in an image can be either reduced, or completely eliminated, by transforming the image into the frequency domain using a Fourier transform, and then shifting the spectrum towards the origin. After this, the spectrum is transformed back to the spatial domain using the inverse Fourier transform and finally the phase is extracted using the arctangent function. However, it is a common concern that the spectrum can be shifted only by an integer number, meaning that the phase wrap reduction is often not optimal. In this paper we propose an algorithm than enables the spectrum to be frequency shifted by a rational number. The principle of the proposed method is confirmed both by using an initial computer simulation and is subsequently validated experimentally on real fringe patterns. The technique may offer in some cases the prospects of removing the necessity for a phase unwrapping process altogether and/or speeding up the phase unwrapping process. This may be beneficial in terms of potential increases in signal recovery robustness and also for use in time-critical applications

Providing Personalized Guidance in Arithmetic Problem Solving

Supervising a student's resolution of an arithmetic word problem is a cumbersome task. Di erent students may use di erent lines of reasoning to reach the nal solution, and the assistance provided should be consistent with the resolution path that the student has in mind. In addition, further learning gains can be achieved if the previous student's background is also considered in the process. In this paper, we outline a relatively simple method to adapt the hints given by an Intelligent Tutoring System to the line of reasoning that the student is currently following. We also outline possible extensions to build a model of the student's most relevant skills, by tracking user's actions

Improved Action Unit Detection Based on a Hybrid Model

Facial action detection and facial expression recognition are two closely intertwined problems in behavior analysis. This paper presents evidence that model architectures designed for facial expression recognition can be seamlessly adapted for the action units detection task, taking advantage of the structural similarity between the two problems. As a sample case, we have adapted the Pyramid crOss-fuSion TransformER (POSTER) model for action unit detection by adjusting the architecture to handle a multilabel problem with one output per action unit. Then, we tuned the training parameters and retrained the model to achieve state-of-the-art performance on two widely used datasets: DISFA and BP4D. The results obtained with a standard 3-fold cross-validation setup show an average F1 score of 67.8% for DISFA and 65.5% for BP4D. These results outperform state-of-the-art models for AU detection, support the effectiveness of the approach, and suggest placing higher efforts on adapting existing architectures to leverage the synergies between facial expression recognition and action unit detection

Cognitive Reasoning and Inferences through Psychologically based Personalised Modelling of Emotions Using Associative Classifiers

Presentation available from: http://www.slideshare.net/AladdinAyesh/v1-cognitive-informaticspresentation201

Adapting to the EHEA: a Case Study

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Combining Supervised and Unsupervised Learning to Discover Emotional Classes

Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation to user reported valence levels (i.e., pleasantness) for each signal, refining the original set of target classes