Francisco Sepulveda | University of Essex (original) (raw)

Uploads

Papers by Francisco Sepulveda

Computers & Graphics, 2004

The utilisation of emotional information in computer systems that interact with humans has become... more The utilisation of emotional information in computer systems that interact with humans has become more prevalent during the last few years. The various channels through which emotions are expressed provide valuable information about the way humans think and behave and have been successfully employed to assist the inference mechanism of interactive computer applications. In this paper a novel approach to detect changes in the emotional status of a subject is presented. It is argued that the proposed methodology will be able to detect emotional changes in real time utilising physiological measures and a combination of Artificial Neural Networks (ANNs) and statistical mechanisms. Clustering analysis is used to show that the myogram signal was the most suitable attribute to distinguish between two emotional states. Results show that the suggested mechanism is able to accurately distinguish changes from neutral to non-neutral emotional states. Emotional information could be employed to improve user interaction in inhabited environments.

Engineering Applications of Artificial Intelligence, 2007

The mystery surrounding emotions, how they work and how they affect our lives has not yet been un... more The mystery surrounding emotions, how they work and how they affect our lives has not yet been unravelled. Scientists still debate the real nature of emotions, whether they are evolutionary, physiological or cognitive are just a few of the different approaches used to explain affective states. Regardless of the various emotional paradigms, neurologists have made progress in demonstrating that emotion is as, or more, important than reason in the process of making decisions and deciding actions. The significance of these findings should not be overlooked in a world that is increasingly reliant on computers to accommodate to user needs. In this paper, a novel approach for recognizing and classifying positive and negative emotional changes in real time using physiological signals is presented. Based on sequential analysis and autoassociative networks, the emotion detection system outlined here is potentially capable of operating on any individual regardless of their physical state and emotional intensity without requiring an arduous adaptation or pre-analysis phase. Results from applying this methodology on real-time data collected from a single subject demonstrated a recognition level of 71.4% which is comparable to the best results achieved by others through off-line analysis. It is suggested that the detection mechanism outlined in this paper has all the characteristics needed to perform emotion recognition in pervasive computing.

Environmental Health Perspectives, 2007

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008

In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroenc... more In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2-D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.

This paper is part of a project whose aim is the implementation of closed-loop control of ankle a... more This paper is part of a project whose aim is the implementation of closed-loop control of ankle angular position during functional electrical stimulation (FES) assisted standing in paraplegic subjects using natural sensory information. In this paper, a neural fuzzy (NF) model is implemented to extract angular position information from the electroneurographic signals recorded from muscle afferents using cuff electrodes in an animal model. The NF model, named dynamic nonsingleton fuzzy logic system is a Mamdani-like fuzzy system, implemented in the framework of recurrent neural networks. The fuzzification procedure implemented was the nonsingleton technique which has been shown in previous works to be able to take into account the uncertainty in the data. The proposed algorithm was tested in different situations and was able to predict reasonably well the ankle angular trajectories especially for small excursions (as during standing) and when the stimulation sites are far from the registration sites. This suggests it may be possible to use activity from muscle afferents recorded with cuff electrodes for FES closed-loop control of ankle position during quite standing.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2002

Closed-loop functional electrical stimulation (FES) applications depend on sensory feedback, thus... more Closed-loop functional electrical stimulation (FES) applications depend on sensory feedback, thus, it is important to continuously investigate new methods to obtain reliable feedback signals. The objective of the present paper was to examine the feasibility of using an artificial neural network (ANN) to predict joint angle from whole nerve cuff recordings of muscle afferent activity within a physiological range of motion. Furthermore, we estimated how small changes in joint angle that can be detected from the nerve cuff recordings. Neural networks were tested with data obtained from ten acute rabbit experiments in simulated, on-line experiments. The electroneurograms (ENG) of the tibial and peroneal nerves were recorded during passive ankle joint rotation. To decrease the joint angle prediction error with new rabbit data, we attempted to pretune the nerve signals and re-trained the ANNs with the pretuned data. With these procedures we were able to compensate for interrabbit variability. On average the mean prediction errors were less than 2.0° (a total excursion of 20°) and we were able to predict joint angles from muscle afferent activity with accuracy close to the best-estimated angular resolution. The angular resolution was found to depend on the initial joint angle and the actual step size taken and we found that there was a low probability of detecting joint angle changes less than 1.5°. We thus suggest that muscle afferent activity is applicable as feedback in real-time closed-loop control, when the motion speed is restricted and when the movement is limited to a portion of the joint's physiological range.