Use of the Choquet Integral for Combination of Classifiers in P300 Based Brain-Computer Interface

One of the key issues in the development of braincomputer interfaces (BCIs) is the improvement of their current information transfer rate. In order to achieve that objective at least two aspects of BCI design should be considered: classification accuracy and protocol specification. In this paper we show how combination of classifiers using fuzzy measures and the Choquet integral can be applied to the context of EEG-based BCI and study whether its use, together with an appropriate application protocol, can lead to an increase in the information transfer rate

Comparison of two different classifiers for mental tasks-based Brain-Computer Interface: MLP Neural Networks vs. Fuzzy Logic

This study is devoted to the classification of fourclass mental tasks data for a Brain-Computer Interface protocol. In such view we adopted Multi Layer Perceptron Neural Network (MLP) and Fuzzy C-means analysis for classifying: left and right hand movement imagination, mental subtraction operation and mental recitation of a nursery rhyme. Five subjects participated to the experiment in two sessions recorded in distinct days. Different parameters were considered for the evaluation of the performances of the two classifiers: accuracy, that is, percentage of correct classifications, training time and size of the training dataset. The results show that even if the accuracies of the two classifiers are quite similar, the MLP classifier needs a smaller training set to reach them with respect to the Fuzzy one. This leads to the preference of MLP for the classification of mental tasks in Brain Computer Interface protocols

Societal issues concerning the application of artificial intelligence in medicine

Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

Development and evaluation of a novel low-cost sensor-based knee flexion angle measurement system

Background Knee injuries play a large part of musculoskeletal traumas in sporting activities and the rehabilitation can require a long period, for both the patients and the specialists, to restore healthy condition. A reliable, portable and low cost system that could allow quick, simple and effective measures of knee flexion angles, would greatly improve the evaluation of the rehabilitation process and the subsequent planning procedure, with meaningful reduction of the recovery time and cost. Methods It is proposed a novel tool for the non-stop measurements of the knee flexion angles, based on the adoption of an elastic sensor embedded in an easy-to-realize wearable kneepad. We fully characterized this tool in terms of accuracy, repeatability and reliability of the measure, and validated it against the gold-standard Vicon. Results Our tool demonstrated good reproducibility and repeatability among testers (mean Range of measures = 5.82° ± 1.93°) and high accuracy (root mean square error <1.28°), together with a good reliability (Intraclass Correlation Coefficient between 0.80 and 0.91). Conclusions The proposed tool demonstrates good performances, is portable, cheap, easy to use, and allows automatic measurements, so to be a valuable system for accurate non-stop measurement of knee angles

Describing different brain computer interface systems through a unique model: a UML implementation

All the protocols currently implemented in brain computer interface (BCI) experiments are characterized by different structural and temporal entities. Moreover, due to the lack of a unique descriptive model for BCI systems, there is not a standard way to define the structure and the timing of a BCI experimental session among different research groups and there is also great discordance on the meaning of the most common terms dealing with BCI, such as trial, run and session. The aim of this paper is to provide a unified modeling language (UML) implementation of BCI systems through a unique dynamic model which is able to describe the main protocols defined in the literature (P300, mu-rhythms, SCP, SSVEP, fMRI) and demonstrates to be reasonable and adjustable according to different requirements. This model includes a set of definitions of the typical entities encountered in a BCI, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. This last represents an innovation with respect to the models already proposed in the literature. The UML documentation and the possibility of adapting this model to the different BCI systems built to date, make it a basis for the implementation of new systems and a mean for the unification and dissemination of resources. The model with all the diagrams and definitions reported in the paper are the core of the body language framework, a free set of routines and tools for the implementation, optimization and delivery of cross-platform BCI systems

UML model for describing Wireless Body Area Networks

Body Area Network (BAN) and Wireless BAN (WBAN) systems lack a unique description model for the identification of all the components and the features that characterize them. This results in a disadvantage since a formalization tool would favor standardization and would seriously help in the implementation of building systems. In this paper we successfully used the Unified Modeling Language (UML) to describe a WBAN which measures hand joint movements, and we demonstrated how UML can be successfully adopted to design a model for the description of such network

Combination of classifiers using the fuzzy integral for uncertainty identification and subject specific optimization - application to brain-computer interface

In this paper we propose a framework for combination of classifiers using fuzzy measures and integrals that aims at providing researchers and practitioners with a simple and structured approach to deal with two issues that often arise in many pattern recognition applications: (i) the need for an automatic and user-specific selection of the best performing classifier or, better, ensemble of classifiers, out of the available ones; (ii) the need for uncertainty identification which should result in an abstention rather than an unreliable decision. We evaluate the framework within the context of Brain-Computer Interface, a field in which abstention and intersubject variability have a remarkable impact. Analysis of experimental data relative to five subjects shows that the proposed system is able to answer such need

Introducing NPXLab 2010: a tool for the analysis and optimization of P300 based Brain-Computer Interfaces

Brain-Computer Interfaces (BCI) are emerging as a powerful tool for providing an alternative way of communication and environment control to severely disabled people. Among these systems, P300-based BCIs are widely diffused as they are easy to manage and do not require a training for the subjects. These systems, however, are still too slow so that they are actually used only by those patients that are unable to control any muscle. It is possible to improve their performances, but many different analyses need to be performed. Here a set of tools are described for the analysis and optimization of this class of BCI protocols that allow increasing the performances of such systems

Performances evaluation and optimization of brain computer interface systems in a copy spelling task

The evaluation of the performances of brain-computer interface (BCI) systems could be difficult as a standard procedure does not exist. In fact, every research team creates its own experimental protocol (different input signals, different trial structure, different output devices, etc.) and this makes systems comparison difficult. Moreover, the great question is whether these experiments can be extrapolated to real world applications or not. To overcome some intrinsic limitations of the most used criteria a new efficiency indicator will be described and used. Its main advantages are that it can predict with a high accuracy the performances of a whole system, a fact that can be used to successfully improve its behavior. Finally, simulations were performed to illustrate that the best system is built by tuning the transducer (TR) and the control interface (CI), which are the two main components of a BCI system, so that the best TR and the best CI do not exist but just the best combination of them

Introducing NPXLab 2010: A tool for the analysis and optimization of P300 based brain-computer interfaces

Brain-Computer Interfaces (BCI) are emerging as a powerful tool for providing an alternative way of communication and environment control to severely disabled people. Among these systems, P300-based BCIs are widely diffused as they are easy to manage and do not require a training for the subjects. These systems, however, are still too slow so that they are actually used only by those patients that are unable to control any muscle. It is possible to improve their performances, but many different analyses need to be performed. Here a set of tools are described for the analysis and optimization of this class of BCI protocols that allow increasing the performances of such systems