Riccardo Poli

Riccardo Poli
University of Essex · School of Computer Science and Electronic Engineering

PhD Biomedical Engineering

About

492
Publications
86,947
Reads
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17,065
Citations
Introduction
We have openings for two postdoctoral researchers with an interest in Brain-Computer Interfaces and Neural Engineering. The advert should appear in Researchgate soon. In the meantime, please see https://www.jobs.ac.uk/job/BYL975/senior-research-officer
Additional affiliations
September 2001 - present
University of Essex
September 2001 - present
University of Essex
Position
  • Professor (Full)
September 1994 - September 2001
University of Birmingham

Publications

Publications (492)
Article
Full-text available
Making decisions is an important aspect of people’s lives. Decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse consequences. Yet, such decisions have often to be made within a very short period of time and with limited information. This can result in decreased accuracy and efficiency. In this paper, we...
Article
Full-text available
Objective: We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods. Approach: We adapted the meta-learning by biased regularisation algorithm to th...
Article
Full-text available
Background: This study assessed the effectiveness of the NEVERMIND e-health system, consisting of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy, in reducing depressive symptoms among patients diagnosed with severe somatic conditions. Our hypothesis was that th...
Article
The vanishing gradient problem (i.e., gradients prematurely becoming extremely small during training, thereby effectively preventing a network from learning) is a long-standing obstacle to the training of deep neural networks using sigmoid activation functions when using the standard back-propagation algorithm. In this paper, we found that an impor...
Chapter
In our previous work at University of Essex, collaborative brain-computer interfaces (cBCIs) have been used to estimate the decision confidence of individuals from their brain signals and behavioral data, thereby making it possible to improve group decision-making. However, such studies used cBCIs in controlled lab conditions, where users were disc...
Article
Full-text available
Neurotechnologies combine neuroscience and engineering to create tools for studying, repairing, and enhancing brain function. Traditionally, researchers have used neurotechnologies, such as Brain-Computer Interfaces (BCIs), as assistive devices, for example to allow locked-in patients to communicate. In the last few decades, non-invasive brain imag...
Article
Full-text available
In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to...
Article
Full-text available
Objective: In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this tudy, we analyse Electroencephalography (EEG) data from 68 participants undertaking...
Article
Objective: Semantic decoding refers to the identification of semantic concepts from recordings of an individual's brain activity. It has been previously reported in fMRI and EEG. We investigate whether semantic decoding is possible with functional near-infrared spectroscopy (fNIRS). Specifically, we attempt to differentiate between the semantic ca...
Preprint
Full-text available
In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide...
Article
Full-text available
INTRODUCTION: Traditional personalised modelling typically requires sufficient personal data for training. This is a challenge in healthcare contexts, e.g. when using smartphones to predict well-being. OBJECTIVE: A method to produce incremental patient-specific models and forecasts even in the early stages of data collection when the data are spor...
Conference Paper
We have uncovered serious flaws in handling EEG signals with a decreased rank in implementations of the common spatial patterns (CSP). The CSP algorithm assumes covariance matrices of the signal to have full rank. However, preprocessing techniques, such as artifact removal using independent component analysis, may decrease the rank of the signal, l...
Article
Full-text available
Background: Depressive symptoms are common in individuals suffering from severe somatic conditions. There is a lack of interventions and evidence-based interventions aiming to reduce depressive symptoms in patients with severe somatic conditions. The aim of the NEVERMIND project is to address these issues and provide evidence by testing the NEVERM...
Conference Paper
Smartphones and wearable sensors are increasingly used for personalised prediction and management in healthcare contexts. Personalisation requires tuning/learning a model of the user. However, traditional machine learning approaches for personalised modelling typically require the availability of sufficient personal data of a suitable nature for tr...
Article
Full-text available
We present the SurfacE Electromyographic with hanD kinematicS (SEEDS) database. It contains electromyographic (EMG) signals and hand kinematics recorded from the forearm muscles of 25 non-disabled subjects while performing 13 different movements at normal and slow-paced speeds. EMG signals were recorded with a high-density 126-channel array centere...
Conference Paper
Full-text available
We present a two-layered collaborative Brain-Computer Interface (cBCI) to aid groups making decisions under time constraints in a realistic video surveillance setting - the very first cBCI application of this type. The cBCI first uses response times (RTs) to estimate the decision confidence the user would report after each decision. Such an estimat...
Article
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[This corrects the article DOI: 10.1371/journal.pone.0212935.].
Article
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Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participa...
Article
Full-text available
Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technol...
Article
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The field of brain–computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...]
Conference Paper
Full-text available
Game balancing has been an important area of academic research in the past few years, with various methods of approaching the task being proposed. At this point in time, however , industry impact has been minimal, with these approaches appearing overwhelming or expensive to game designers and developers. The work presented in this paper takes one o...
Conference Paper
Full-text available
This paper aims to show a glimpse of the potential benefits of using genetic algorithms (GA's) in EEG data analysis. The system attempts machine-learning based classification of a mental state reached during competitive gameplay and an idle state. EEG activity from a large number of experienced players of League of Legends has been recorded during...
Conference Paper
Full-text available
Predicting the severity and onset of depressive symptoms is of great importance. User-specific models have better performance than a general model but require significant amounts of training data from each individual, which is often impractical to obtain. Even when this is possible, there is a significant lag between the beginning of the data-colle...
Conference Paper
Electromyographic (EMG) recordings of muscle activity using monopolar electrodes suffer from poor spatial resolution due to the crosstalk from neighbouring muscles. This effect has mainly been studied on surface EMG recordings. Here, we use Principal Component Analysis (PCA) to reduce the crosstalk in recordings from unipolar epimysial electrodes i...
Preprint
Full-text available
Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participa...
Chapter
Recent work has shown that genetic algorithms are a good choice for use in game design, particularly for finding improved versions of a game’s parameters to better fit a designer’s requirements. A significant issue with this approach to game optimisation is the very long time it can take to evaluate fitness, since this requires running the target g...
Article
Full-text available
Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic vis...
Conference Paper
Genetic Algorithms (GAs) can find game parameters that fit a designer's requirements. An issue with this is the long time taken to evaluate fitness, as this requires running the game many times. Here we use fitness predictors, currently neural networks, to speed up the process by reducing the number of fitness evaluations. The predictors are traine...
Conference Paper
Assessment and recognition of perceived well-being has wide applications in the development of assistive healthcare systems for people with physical and mental disorders. In practical data collection, these systems need to be less intrusive, and respect users' autonomy and willingness as much as possible. As a result, self-reported data are not nec...
Article
Full-text available
The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images...
Conference Paper
Full-text available
This paper presents a hybrid collaborative brain- computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained...
Conference Paper
Games, particularly online games, have an ongoing requirement to exhibit the ability to react to player behaviour and change their mechanics and available tools to keep their audience both entertained and feeling that their strategic choices and in-game decisions have value. Game designers invest time both gathering data and analysing it to introdu...
Conference Paper
Full-text available
In the recent years, collaborative Brain-Computer Interfaces (cBCIs) have shown the potential to be used in the context of neuroergonomics to augment human performance, for example in decision making. This study proposes an innovative hybrid cBCI to augment group performance in decision making.
Conference Paper
Full-text available
Collaborative brain-computer interfaces (cBCIs) have shown potential to improve group decisions with visual stimuli. This paper proposes a cBCI that assists and improves group decisions in a speech perception task. Neural features extracted from left-temporal-lobe EEG signals and response times were used to estimate the confidence of each individua...
Article
Full-text available
Objective: We aimed at improving group performance in a challenging visual search task via a hybrid collaborative brain-computer interface (cBCI). Methods: Ten participants individually undertook a visual search task where a display was presented for 250 ms, and they had to decide whether a target was present or not. Local temporal correlation c...
Conference Paper
Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first ord...
Article
Objective: The N2pc event-related potential (ERP) appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located.We explored the feasibility of using it to extract information on the spatial location of targets in aerial images shown by means of a rapid serial visual presentation (RSVP) proto...
Article
Full-text available
In this paper, we analyse the Event-Related Potentials (ERPs) produced by cuts where the scenes before and after the cut are narratively related. In tests with 6 participants and 930 cuts from 5 Hollywood feature movies we found that cuts produce a large negative ERP with an onset 100 ms after a cut and a duration of 600 ms, distributed over a very...
Conference Paper
Full-text available
In this paper we use a collaborative brain- computer interface to integrate the decision confidence of multiple non-communicating observers as a mechanism to improve group decisions. In recent research we tested this idea with the decisions associated with a simple visual matching task and found that a collaborative BCI can outperform group decisio...
Conference Paper
Full-text available
Detecting a target in a complex environment can be a difficult task, both for a single individual and a group, es- pecially if the scene is very rich of structure and there are strict time constraints. In recent research, we have demonstrated that collaborative Brain-Computer Interfaces (cBCIs) can use neural signals and response times to estimate...
Article
Event-Related Potentials (ERPs) are electrical signals produced by the brain in response to external stimuli. Due to the enormous noise affecting them, traditionally, the analysis of ERPs has relied of averaging the signals recorded in many repetitions of an experiment. However, while averaging helps to improve the signal to noise ratio of an ERP,...
Article
Full-text available
The parsimony pressure method is perhaps the simplest and most fre-quently used method to control bloat in genetic programming. In this chapter we first reconsider the size evolution equation for genetic programming developed in [28] and rewrite it in a form that shows its direct relationship to Price's theorem. We then use this new formulation to...
Conference Paper
Full-text available
The N2pc event-related potential appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located. In this paper, we propose a 2-user collaborative brain-computer interface that exploits this component for the automatic localisation of specific lateral targets in real aerial images displayed by...
Article
Large sparse symmetric matrices are typical characteristics of the linear systems found in various scientific and engineering disciplines, such as fluid mechanics, structural engineering, finite element analysis, and network analysis. In all such systems, the performance of solvers crucially depends on the sum of the distance of each matrix element...
Article
Full-text available
We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a sim...
Article
Large sparse symmetric matrix problems arise in a number of scientific and engineering fields such as fluid mechanics, structural engineering, finite element analysis and network analysis. In all such problems, the performance of solvers depends critically on the sum of the row bandwidths of the matrix, a quantity known as envelope size. This can b...
Article
Exploration and exploitation are considered essential notions in evolutionary algorithms. However, a precise interpretation of what constitutes exploration or exploitation is clearly lacking and so are specific measures for characterising such notions. In this paper, we start addressing this issue by presenting new measures that can be used as indi...
Chapter
Full-text available
The study of complex adaptive systems is among the key modern tasks in science. Such systems show radically different behaviours at different scales and in different environments, and mathematical modelling of such emergent behaviour is very difficult, even at the conceptual level. We require a new methodology to study and understand complex, emerg...
Chapter
The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called machine intelligence (Turing 1948, 1950).
Chapter
The parsimony pressure method is perhaps the simplest and most frequently used method to control bloat in genetic programming (GP). In this chapter we first reconsider the size evolution equation for genetic programming developed in Poli andMcPhee (Evol Comput 11(2):169-206, 2003) and rewrite it in a form that shows its direct relationship to Price...
Conference Paper
Full-text available
In this paper, we propose a collaborative brain-computer interface for the automatic discrimination of images containing specific targets. When a user looks at a stream of images that are displayed using the rapid serial visual presentation protocol, images containing targets elicit a P300 event-related potential that can be detected. This allows t...
Conference Paper
A significant proportion of patients affected by Parkinson's disease (PD) suffer from drug-resistant tremor, which often eventually progresses to disabling and distressing levels. Recent literature on PD indicates that such tremor is associated with corresponding oscillations in the thalamus. In this research, we wanted to explore the possibility t...
Article
Full-text available
Abstract Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify...
Conference Paper
Full-text available
In rapid serial visual presentation (RSVP) images shown extremely rapidly can still be parsed by the visual system, and the detection of specific targets triggers specific EEG response. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has proven speed-ups in sifting through satellite images when EEG signals of an intelli...
Conference Paper
Full-text available
We explored the possibility of controlling a spacecraft simulator using an analogue Brain-Computer Interface (BCI) for 2-D pointer control. This is a difficult task, for which no previous attempt has been reported in the literature. Our system relies on an active display which produces event-related potentials (ERPs) in the user's brain. These are...
Conference Paper
Full-text available
In the presence of complex stimuli, in the absence of sufficient time to complete the visual parsing of a scene, or when attention is divided, an observer can only take in a subset of the features of a scene, potentially leading to poor decisions. In this paper we look at the possibility of integrating the percepts from multiple non-communicating o...
Article
Full-text available
Abstract Modelling the behaviour of algorithms is the realm of Evolutionary Algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realist...
Article
We propose the use of genetic programming (GP) as a means to evolve brain–computer interfaces for mouse control. Our objective is to synthesise complete systems, which analyse electrical brain signals and directly transform them into pointer movements, almost from scratch, the only input provided by us in the process being the set of visual stimuli...