Sviluppo di un sistema per gestire e partizionare segnali neurali

La Brain Computer Interface, con l’acronimo BCI, è un mezzo per la comunicazione tra cervello e macchina. La comunicazione si basa sulla emanazione di segnali elettrici del cervello che vengono rilevati da un dispositivo, il quale invia i segnali digitalizzati ad un elaboratore. I segnali elettrici, chiamati EEG, permettono al cervello di regolare la comunicazione tra le diverse cellule neurali. La BCI intercetta questi segnali e, previa elaborazione, permette di ottenere diversi diagrammi, detti metriche, per poter misurare, sotto svariati aspetti, il funzionamento del cervello. Le ricerche scientifiche sulle EEG hanno rilevato una correlazione tra i segnali elettrici nel cervello di un soggetto con il suo livello di performance e stato emotivo. È quindi possibile comprendere, tramite una serie di parametri, come la mente dei soggetti reagisce a stimoli esterni di svariata tipologia durante lo svolgimento di un’attività. L’elaboratore, che riceve il segnale dalla BCI, è il componente che si occupa di trasformare i segnali elettrici, generati dal cervello e digitalizzati, in risultati facilmente interpretabili dall’utente. Elaborare i segnali EEG in tempo reale porta a dover utilizzare algoritmi creati appositamente per questo scopo e specifici perle metriche preposte. Lo scopo di questa tesi è quello di presentare un progetto sullo sviluppo della fase di smistamento dei dati ricevuti dall’elaboratore. Nel contempo si fornirà una conoscenza scientifica minima per comprendere le scelte fatte. Tale progetto è stato reso possibile dalla collaborazione con l’azienda Vibre, che si dedica allo sviluppo di un sistema comprendente BCI ed elaboratore.

Analisi e utilizzo di tecniche di stimolazione epidurale per il ripristino dell’attività locomotoria in soggetti con lesioni spinali

La lesione del midollo spinale (LM) è una complessa condizione fisica che racchiude in sé sfide di carattere biomedico nonché etico-giuridico. La complessità della LM nonché la diversificazione delle esperienze dei singoli soggetti affetti da LM rendono questo un topic di grande interesse per la ricerca biomedicale, in relazione a nuovi metodi di cura e di riabilitazione dei soggetti. In particolare, la sinergia tra i saperi medico, informatici e ingegneristici ha permesso di sviluppare nuove tecnologie di comunicazione e di controllo neurologico e motorio che, capaci di sopperire a deficit cerebrali e/o motori causati da LM, consentono ai pazienti di avere una qualità di vita sensibilmente migliore, anche in termini di autonomia. Tra queste nuove tecnologie assistive primeggiano per efficacia e frequenza di utilizzo le Brain Computer Interfaces (BCI), strumenti ingegneristici che, attraverso la misurazione e l’analisi di segnali provenienti dall’attività cerebrale, traducono il segnale registrato in specifici comandi, rappresentando per l’utente con LM un canale di comunicazione con l’ambiente esterno, alternativo alle normali vie neurali. In questo elaborato l’analisi di due sperimentazioni, una su scimmia l’altra su uomo, entrambi affetti da LM, con differenti sistemi di monitoraggio dell’attività neurale, ha permesso di evidenziare un limite della ricerca sul topic: nonostante i promettenti risultati ottenuti su primati non umani, il carattere invasivo del sistema BCI–EES rende difficile traslare la sperimentazione su uomo. La sperimentazione su LM pone delle sfide anche dal punto di vista etico: sebbene siano auspicati lo sviluppo e l’applicazione di metodi alternativi alla sperimentazione animale, l’impiego di primati non umani appare ancora una scelta obbligata nel campo della ricerca di soluzioni terapeutiche finalizzate al ripristino della funzione locomotoria, per via della stretta affinità in termini di conformazione fisica, genetica e anatomica.

Effect of instructive visual stimuli on neurofeedback training for motor imagery-based braincomputer interface

Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills.

Um sistema inteligente de classificação de sinais de EEG para Interface Cérebro-Computador

The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

Enabling computer access: Introduction to the Test of Mouse Proficiency

As the use of technological devices in everyday environments becomes more prevalent, it is clear that access to these devices has become an important aspect of occupational performance. Children are increasingly required to competently manipulate technology such as the computer to fulfil occupational roles of student and player. Occupational therapists are in a position to facilitate the successful interface between children and standard computer technologies. The literature has supported the use of direct manipulation interfaces in computing that requires mastery of devices such as the mouse. Identification of children likely to experience difficulties with mouse use will inform the development of appropriate methods of intervention promoting mouse skill and further enhance participation in occupational tasks. The aim of this paper is to discuss the development of an assessment of mouse proficiency for children. It describes the construction of the assessment, the content of the test, and its content validity.

Assessing childrens competence in computer interactions: Preliminary reliability and validity of the test of mouse proficiency

The Test of Mouse Proficiency (TOMP) was developed to assist occupational therapists and education professionals assess computer mouse competency skills in children from preschool to upper primary (elementary) school age. The preliminary reliability and validity of TOMP are reported in this paper. Methods used to examine the internal consistency, test-retest reliability, and criterion- and construct-related validity of the test are elaborated. In the continuing process of test refinement, these preliminary studies support to varying degrees the reliability and validity of TOMP. Recommendations for further validation of the assessment are discussed along with indications for potential clinical application.

Hand gesture recognition system based in computer vision and machine learning : applications on human-machine interaction

Sendo uma forma natural de interação homem-máquina, o reconhecimento de gestos implica uma forte componente de investigação em áreas como a visão por computador e a aprendizagem computacional. O reconhecimento gestual é uma área com aplicações muito diversas, fornecendo aos utilizadores uma forma mais natural e mais simples de comunicar com sistemas baseados em computador, sem a necessidade de utilização de dispositivos extras. Assim, o objectivo principal da investigação na área de reconhecimento de gestos aplicada à interacção homemmáquina é o da criação de sistemas, que possam identificar gestos específicos e usálos para transmitir informações ou para controlar dispositivos. Para isso as interfaces baseados em visão para o reconhecimento de gestos, necessitam de detectar a mão de forma rápida e robusta e de serem capazes de efetuar o reconhecimento de gestos em tempo real. Hoje em dia, os sistemas de reconhecimento de gestos baseados em visão são capazes de trabalhar com soluções específicas, construídos para resolver um determinado problema e configurados para trabalhar de uma forma particular. Este projeto de investigação estudou e implementou soluções, suficientemente genéricas, com o recurso a algoritmos de aprendizagem computacional, permitindo a sua aplicação num conjunto alargado de sistemas de interface homem-máquina, para reconhecimento de gestos em tempo real. A solução proposta, Gesture Learning Module Architecture (GeLMA), permite de forma simples definir um conjunto de comandos que pode ser baseado em gestos estáticos e dinâmicos e que pode ser facilmente integrado e configurado para ser utilizado numa série de aplicações. É um sistema de baixo custo e fácil de treinar e usar, e uma vez que é construído unicamente com bibliotecas de código. As experiências realizadas permitiram mostrar que o sistema atingiu uma precisão de 99,2% em termos de reconhecimento de gestos estáticos e uma precisão média de 93,7% em termos de reconhecimento de gestos dinâmicos. Para validar a solução proposta, foram implementados dois sistemas completos. O primeiro é um sistema em tempo real capaz de ajudar um árbitro a arbitrar um jogo de futebol robótico. A solução proposta combina um sistema de reconhecimento de gestos baseada em visão com a definição de uma linguagem formal, o CommLang Referee, à qual demos a designação de Referee Command Language Interface System (ReCLIS). O sistema identifica os comandos baseados num conjunto de gestos estáticos e dinâmicos executados pelo árbitro, sendo este posteriormente enviado para um interface de computador que transmite a respectiva informação para os robôs. O segundo é um sistema em tempo real capaz de interpretar um subconjunto da Linguagem Gestual Portuguesa. As experiências demonstraram que o sistema foi capaz de reconhecer as vogais em tempo real de forma fiável. Embora a solução implementada apenas tenha sido treinada para reconhecer as cinco vogais, o sistema é facilmente extensível para reconhecer o resto do alfabeto. As experiências também permitiram mostrar que a base dos sistemas de interação baseados em visão pode ser a mesma para todas as aplicações e, deste modo facilitar a sua implementação. A solução proposta tem ainda a vantagem de ser suficientemente genérica e uma base sólida para o desenvolvimento de sistemas baseados em reconhecimento gestual que podem ser facilmente integrados com qualquer aplicação de interface homem-máquina. A linguagem formal de definição da interface pode ser redefinida e o sistema pode ser facilmente configurado e treinado com um conjunto de gestos diferentes de forma a serem integrados na solução final.

The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Diagnosing dementia and normal aging: clinical relevance of brain ratios and cognitive performance in a Brazilian sample

The main objective of the present study was to evaluate the diagnostic value (clinical application) of brain measures and cognitive function. Alzheimer and multiinfarct patients (N = 30) and normal subjects over the age of 50 (N = 40) were submitted to a medical, neurological and cognitive investigation. The cognitive tests applied were Mini-Mental, word span, digit span, logical memory, spatial recognition span, Boston naming test, praxis, and calculation tests. The brain ratios calculated were the ventricle-brain, bifrontal, bicaudate, third ventricle, and suprasellar cistern measures. These data were obtained from a brain computer tomography scan, and the cutoff values from receiver operating characteristic curves. We analyzed the diagnostic parameters provided by these ratios and compared them to those obtained by cognitive evaluation. The sensitivity and specificity of cognitive tests were higher than brain measures, although dementia patients presented higher ratios, showing poorer cognitive performances than normal individuals. Normal controls over the age of 70 presented higher measures than younger groups, but similar cognitive performance. We found diffuse losses of tissue from the central nervous system related to distribution of cerebrospinal fluid in dementia patients. The likelihood of case identification by functional impairment was higher than when changes of the structure of the central nervous system were used. Cognitive evaluation still seems to be the best method to screen individuals from the community, especially for developing countries, where the cost of brain imaging precludes its use for screening and initial assessment of dementia.

Therapeutic potential of computer to cerebral cortex implantable devices

In this article, an overview of some of the latest developments in the field of cerebral cortex to computer interfacing (CCCI) is given. This is posed in the more general context of Brain-Computer Interfaces in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system. Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bidirectionally with technology and the internet. A view is taken as to the prospects for the future for CCCI, in terms of its broad therapeutic role.

Generic control interface for networked haptic virtual environments

As Virtual Reality pushes the boundaries of the human computer interface new ways of interaction are emerging. One such technology is the integration of haptic interfaces (force-feedback devices) into virtual environments. This modality offers an improved sense of immersion to that achieved when relying only on audio and visual modalities. The paper introduces some of the technical obstacles such as latency and network traffic that need to be overcome for maintaining a high degree of immersion during haptic tasks. The paper describes the advantages of integrating haptic feedback into systems, and presents some of the technical issues inherent in a networked haptic virtual environment. A generic control interface has been developed to seamlessly mesh with existing networked VR development libraries.

Practical interface experiments with implant technology

In this paper results are shown to indicate the efficacy of a direct connection between the human nervous system and a computer network. Experimental results obtained thus far from a study lasting for over 3 months are presented, with particular emphasis placed on the direct interaction between the human nervous system and a piece of wearable technology. An overview of the present state of neural implants is given, as well as a range of application areas considered thus far. A view is also taken as to what may be possible with implant technology as a general purpose human-computer interface for the future.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Signal processing and information fusing algorithms for the synthesis of an alternative electromyogram/eye gaze tracking computer cursor control system

This research pursued the conceptualization and real-time verification of a system that allows a computer user to control the cursor of a computer interface without using his/her hands. The target user groups for this system are individuals who are unable to use their hands due to spinal dysfunction or other afflictions, and individuals who must use their hands for higher priority tasks while still requiring interaction with a computer. ^ The system receives two forms of input from the user: Electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an Eye Gaze Tracking (EGT) system. In order to produce reliable cursor control from the two forms of user input, the development of this EMG/EGT system addressed three key requirements: an algorithm was created to accurately translate EMG signals due to facial movements into cursor actions, a separate algorithm was created that recognized an eye gaze fixation and provided an estimate of the associated eye gaze position, and an information fusion protocol was devised to efficiently integrate the outputs of these algorithms. ^ Experiments were conducted to compare the performance of EMG/EGT cursor control to EGT-only control and mouse control. These experiments took the form of two different types of point-and-click trials. The data produced by these experiments were evaluated using statistical analysis, Fitts' Law analysis and target re-entry (TRE) analysis. ^ The experimental results revealed that though EMG/EGT control was slower than EGT-only and mouse control, it provided effective hands-free control of the cursor without a spatial accuracy limitation, and it also facilitated a reliable click operation. This combination of qualities is not possessed by either EGT-only or mouse control, making EMG/EGT cursor control a unique and practical alternative for a user's cursor control needs. ^