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.

Analisi delle caratteristiche di dispositivi per il brain computer interface

La brain computer interface sono state create per sopperire ai problemi motori e di linguistica nei pazienti che, a causa di malattie neurodegenerative o in seguito a traumi, sono paralizzate o non riescono a parlare. Vengono prese in questione le caratteristiche di un sistema per le BCI analizzando ogni blocco funzionale del sistema. Si fa visione delle applicazioni tra le quali la possibilità di far muovere un arto bionico a una donna tetraplegica e la possibilità di far scrivere una parola su uno schermo a un paziente non in grado di comunicare verbalmente. Si mettono in luce i problemi annessi a questo campo e i possibili sviluppi.

Utilizzo dell'elettroencefalografia per la brain-computer interface

Con Brain-Computer Interface si intende un collegamento diretto tra cervello e macchina, che essa sia un computer o un qualsiasi dispositivo esterno, senza l’utilizzo di muscoli. Grazie a sensori applicati alla cute del cranio i segnali cerebrali del paziente vengono rilevati, elaborati, classificati (per mezzo di un calcolatore) e infine inviati come output a un device esterno. Grazie all'utilizzo delle BCI, persone con gravi disabilità motorie o comunicative (per esempio malati di SLA o persone colpite dalla sindrome del chiavistello) hanno la possibilità di migliorare la propria qualità di vita. L'obiettivo di questa tesi è quello di fornire una panoramica nell'ambito dell'interfaccia cervello-computer, mostrando le tipologie esistenti, cercando di farne un'analisi critica sui pro e i contro di ogni applicazione, ponendo maggior attenzione sull'uso dell’elettroencefalografia come strumento per l’acquisizione dei segnali in ingresso all'interfaccia.

Sistemi Brain Computer Interface: dalla macchina al paziente

C’è un crescente interesse nella comunità scientifica per l’applicazione delle tecniche della bioingegneria nel campo delle interfacce fra cervello e computer. Questo interesse nasce dal fatto che in Europa ci sono almeno 300.000 persone con paralisi agli arti inferiori, con una età media piuttosto bassa (31 anni), registrandosi circa 5.000 nuovi casi ogni anno, in maggioranza dovuti ad incidenti automobilistici. Tali lesioni traumatiche spinali inducono delle disfunzioni sensoriali a causa dell’interruzione tra gli arti e i centri sopraspinali. Per far fronte a questi problemi gli scienziati si sono sempre più proiettati verso un nuovo settore: il Brain Computer Interaction, ossia un ambito della ricerca volto alla costruzione di interfacce in grado di collegare direttamente il cervello umano ad un dispositivo elettrico come un computer.

A cybernetic theory about computer interfaces and human factors within a framework of technological innovation

This paper is based on the following postulates taken from a book recently published by this author (Sáez-Vacas, 1990(1)): a) technological innovation in a company is understood to be the process and set of changes that the company undergoes as a result of a specific type of technology; b) the incorporation of technology in the company does not necessarily result in innovation, modernization and progress; c) the very words "modernization" and "progress" are completely bereft of any meaning if isolated from the concept of complexity in its broadest sense, including the human factor. Turning to office technology in specific, the problem of managing office technology for business innovation purposes can be likened to the problem of managing third level complexity, following the guidelines of a three-level complexity model proposed by the author some years ago

Study and analysis of the Leap Motion sensor and the Software Development Kit (SDK) for the implementation of visual Human Computer Interfaces

En este proyecto, se presenta un informe técnico sobre la cámara Leap Motion y el Software Development Kit correspondiente, el cual es un dispositivo con una cámara de profundidad orientada a interfaces hombre-máquina. Esto es realizado con el propósito de desarrollar una interfaz hombre-máquina basada en un sistema de reconocimiento de gestos de manos. Después de un exhaustivo estudio de la cámara Leap Motion, se han realizado diversos programas de ejemplo con la intención de verificar las capacidades descritas en el informe técnico, poniendo a prueba la Application Programming Interface y evaluando la precisión de las diferentes medidas obtenidas sobre los datos de la cámara. Finalmente, se desarrolla un prototipo de un sistema de reconocimiento de gestos. Los datos sobre la posición y orientación de la punta de los dedos obtenidos de la Leap Motion son usados para describir un gesto mediante un vector descriptor, el cual es enviado a una Máquina Vectores Soporte, utilizada como clasificador multi-clase.

Closed-Loop Deep Brain Stimulation: Bidirectional Neuroprosthetics for Tremor and BCI

Thesis (Ph.D.)--University of Washington, 2016-06

Which physiological components are more suitable for visual ERP based brain-computer interface?:A preliminary MEG/EEG study

We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain-computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (

Neurorehabilitation of hand functions using brain computer interface

Introduction: Brain computer interface (BCI) is a promising new technology with possible application in neurorehabilitation after spinal cord injury. Movement imagination or attempted movement-based BCI coupled with functional electrical stimulation (FES) enables the simultaneous activation of the motor cortices and the muscles they control. When using the BCI- coupled with FES (known as BCI-FES), the subject activates the motor cortex using attempted movement or movement imagination of a limb. The BCI system detects the motor cortex activation and activates the FES attached to the muscles of the limb the subject is attempting or imaging to move. In this way the afferent and the efferent pathways of the nervous system are simultaneously activated. This simultaneous activation encourages Hebbian type learning which could be beneficial in functional rehabilitation after spinal cord injury (SCI). The FES is already in use in several SCI rehabilitation units but there is currently not enough clinical evidence to support the use of BCI-FES for rehabilitation. Aims: The main aim of this thesis is to assess outcomes in sub-acute tetraplegic patients using BCI-FES for functional hand rehabilitation. In addition, the thesis explores different methods for assessing neurological rehabilitation especially after BCI-FES therapy. The thesis also investigated mental rotation as a possible rehabilitation method in SCI. Methods: Following investigation into applicable methods that can be used to implement rehabilitative BCI, a BCI based on attempted movement was built. Further, the BCI was used to build a BCI-FES system. The BCI-FES system was used to deliver therapy to seven sub-acute tetraplegic patients who were scheduled to receive the therapy over a total period of 20 working days. These seven patients are in a 'BCI-FES' group. Five more patients were also recruited and offered equivalent FES quantity without the BCI. These further five patients are in a 'FES-only' group. Neurological and functional measures were investigated and used to assess both patient groups before and after therapy. Results: The results of the two groups of patients were compared. The patients in the BCI-FES group had better improvements. These improvements were found with outcome measures assessing neurological changes. The neurological changes following the use of the BCI-FES showed that during movement attempt, the activation of the motor cortex areas of the SCI patients became closer to the activation found in healthy individuals. The intensity of the activation and its spatial localisation both improved suggesting desirable cortical reorganisation. Furthermore, the responses of the somatosensory cortex during sensory stimulation were of clear evidence of better improvement in patients who used the BCI-FES. Missing somatosensory evoked potential peaks returned more for the BCI-FES group while there was no overall change in the FES-only group. Although the BCI-FES group had better neurological improvement, they did not show better functional improvement than the FES-only group. This was attributed mainly to the short duration of the study where therapies were only delivered for 20 working days. Conclusions: The results obtained from this study have shown that BCI-FES may induce cortical changes in the desired direction at least faster than FES alone. The observation of better improvement in the patients who used the BCI-FES is a good result in neurorehabilitation and it shows the potential of thought-controlled FES as a neurorehabilitation tool. These results back other studies that have shown the potential of BCI-FES in rehabilitation following neurological injuries that lead to movement impairment. Although the results are promising, further studies are necessary given the small number of subjects in the current study.

Plataforma smartphone para biossensores de espectroscopia de infravermelho próximo

O tema “Plataforma smartphone para biossensores de Espectroscopia de Infravermelho Próximo”, surge no âmbito da instrumentação médica, na área das BCI – Brain Computer Interfaces, devido à necessidade de encontrar um dispositivo portátil, de custo acessível e elevada performance que permita obter informação acerca da actividade neuronal do córtex motor no decorrer duma determinada tarefa. O objectivo do trabalho consiste no desenvolvimento duma sonda capaz de detectar as alterações hemodinâmicas que ocorrem no córtex, bem como toda a instrumentação inerente à aquisição do sinal e transmissão dos dados para um computador, a análise dos dados e por fim o desenvolvimento de uma aplicação em Android para visualização dos resultados. Foi desenvolvida uma banda para a cabeça, composta pela sonda NIRS: LEDs (Light-Emiting Diodes) de 940nm e 660nm e os respectivos fototransístores de detecção, bem como toda a electrónica de condicionamento do sinal captado. Num módulo à parte, alimentado por duas baterias de 9V, encontram-se os circuitos electrónicos onde é possível regular ganhos de amplificação e offsets. Os dados foram adquiridos pelo microcontrolador Arduíno Uno, usando uma taxa de amostragem de 50Hz em cada um dos dois canais utilizados. O controlo do Arduíno foi feito utilizando o LabVIEW. Para o processamento dos dados, visualização e cálculo das concentrações de oxi e desoxi-hemoglobina no sangue recorreu-se ao Matlab. O sistema foi calibrado com recurso a um oxímetro de pulso clínico usado em cinco indivíduos saudáveis. Finalmente o sistema foi testado ao colocar-se o sensor NIRS sobre o córtex motor esquerdo de nove indivíduos saudáveis destros, fazendo-se uma aquisição de dados durante dois minutos. Utilizou-se um paradigma de 10s de repouso seguido de 10s a abrir e fechar a mão. O sistema NIRS conseguiu medir as alterações que ocorrem nas concentrações de oxi e desoxi-hemoglobina devido à actividade motora de abrir e fechar a mão. Dado o princípio físico ser o mesmo do dos oxímetros convencionais, conseguiu-se ainda medir com sucesso a frequência cardíaca e a saturação percentual de oxigénio após a calibração do sensor. As medidas podem ser visualizadas numa aplicação desenvolvida para o Android. Os resultados sugerem que com esta abordagem, este tipo de dispositivo pode estar disponível a baixo custo quer para doentes quer para indivíduos saudáveis, por exemplo em aplicações de telemóvel.

Effects of P300-based BCI use on reported presence in a virtual environment

Brain-computer interfaces (BCIs) are becoming more and more popular as an input device for virtual worlds and computer games. Depending on their function, a major drawback is the mental workload associated with their use and there is significant effort and training required to effectively control them. In this paper, we present two studies assessing how mental workload of a P300-based BCI affects participants" reported sense of presence in a virtual environment (VE). In the first study, we employ a BCI exploiting the P300 event-related potential (ERP) that allows control of over 200 items in a virtual apartment. In the second study, the BCI is replaced by a gaze-based selection method coupled with wand navigation. In both studies, overall performance is measured and individual presence scores are assessed by means of a short questionnaire. The results suggest that there is no immediate benefit for visualizing events in the VE triggered by the BCI and that no learning about the layout of the virtual space takes place. In order to alleviate this, we propose that future P300-based BCIs in VR are set up so as require users to make some inference about the virtual space so that they become aware of it,which is likely to lead to higher reported presence.

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.