A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition

Transient neural assemblies mediated by synchrony in particular frequency ranges are thought to underlie cognition. We propose a new approach to their detection, using empirical mode decomposition (EMD), a data-driven approach removing the need for arbitrary bandpass filter cut-offs. Phase locking is sought between modes. We explore the features of EMD, including making a quantitative assessment of its ability to preserve phase content of signals, and proceed to develop a statistical framework with which to assess synchrony episodes. Furthermore, we propose a new approach to ensure signal decomposition using EMD. We adapt the Hilbert spectrum to a time-frequency representation of phase locking and are able to locate synchrony successfully in time and frequency between synthetic signals reminiscent of EEG. We compare our approach, which we call EMD phase locking analysis (EMDPL) with existing methods and show it to offer improved time-frequency localisation of synchrony.

Exploring combinations of different color and facial expression stimuli for gaze-independent BCIs

Background: Some studies have proven that a conventional visual brain computer interface (BCI) based on overt attention cannot be used effectively when eye movement control is not possible. To solve this problem, a novel visual-based BCI system based on covert attention and feature attention has been proposed and was called the gaze-independent BCI. Color and shape difference between stimuli and backgrounds have generally been used in examples of gaze-independent BCIs. Recently, a new paradigm based on facial expression changes has been presented, and obtained high performance. However, some facial expressions were so similar that users couldn't tell them apart, especially when they were presented at the same position in a rapid serial visual presentation (RSVP) paradigm. Consequently, the performance of the BCI is reduced. New Method: In this paper, we combined facial expressions and colors to optimize the stimuli presentation in the gaze-independent BCI. This optimized paradigm was called the colored dummy face pattern. It is suggested that different colors and facial expressions could help users to locate the target and evoke larger event-related potentials (ERPs). In order to evaluate the performance of this new paradigm, two other paradigms were presented, called the gray dummy face pattern and the colored ball pattern. Comparison with Existing Method(s): The key point that determined the value of the colored dummy faces stimuli in BCI systems was whether the dummy face stimuli could obtain higher performance than gray faces or colored balls stimuli. Ten healthy participants (seven male, aged 21–26 years, mean 24.5 ± 1.25) participated in our experiment. Online and offline results of four different paradigms were obtained and comparatively analyzed. Results: The results showed that the colored dummy face pattern could evoke higher P300 and N400 ERP amplitudes, compared with the gray dummy face pattern and the colored ball pattern. Online results showed that the colored dummy face pattern had a significant advantage in terms of classification accuracy (p < 0.05) and information transfer rate (p < 0.05) compared to the other two patterns. Conclusions: The stimuli used in the colored dummy face paradigm combined color and facial expressions. This had a significant advantage in terms of the evoked P300 and N400 amplitudes and resulted in high classification accuracies and information transfer rates. It was compared with colored ball and gray dummy face stimuli.

Interfacce cervello-computer: Stato dell'arte

Un'interfaccia cervello-computer (BCI: Brain-Computer Interface) è un sistema di comunicazione diretto tra il cervello e un dispositivo esterno che non dipende dalle normali vie di output del cervello, costituite da nervi o muscoli periferici. Il segnale generato dall'utente viene acquisito per mezzo di appositi sensori, poi viene processato e classificato estraendone così le informazioni di interesse che verranno poi utilizzate per produrre un output reinviato all'utente come feedback. La tecnologia BCI trova interessanti applicazioni nel campo biomedico dove può essere di grande aiuto a persone soggette da paralisi, ma non sono da escludere altri utilizzi. Questa tesi in particolare si concentra sulle componenti hardware di una interfaccia cervello-computer analizzando i pregi e i difetti delle varie possibilità: in particolar modo sulla scelta dell'apparecchiatura per il rilevamento della attività cerebrale e dei meccanismi con cui gli utilizzatori della BCI possono interagire con l'ambiente circostante (i cosiddetti attuatori). Le scelte saranno effettuate tenendo in considerazione le necessità degli utilizzatori in modo da ridurre i costi e i rischi aumentando il numero di utenti che potranno effettivamente beneficiare dell'uso di una interfaccia cervello-computer.

Le interfacce neurali e le loro possibili applicazioni nell'assistive technology

Ogni anno si registra un crescente aumento delle persone affette da patologie neurodegenerative come la sclerosi laterale amiotrofica, la sclerosi multipla, la malattia di Parkinson e persone soggette a gravi disabilità motorie dovute ad ictus, paralisi cerebrale o lesioni al midollo spinale. Spesso tali condizioni comportano menomazioni molto invalidanti e permanenti delle vie nervose, deputate al controllo dei muscoli coinvolti nell’esecuzione volontaria delle azioni. Negli ultimi anni, molti gruppi di ricerca si sono interessati allo sviluppo di sistemi in grado di soddisfare le volontà dell’utente. Tali sistemi sono generalmente definiti interfacce neurali e non sono pensati per funzionare autonomamente ma per interagire con il soggetto. Tali tecnologie, note anche come Brain Computer Interface (BCI), consentono una comunicazione diretta tra il cervello ed un’apparecchiatura esterna, basata generalmente sull’elettroencefalografia (EEG), in grado di far comunicare il sistema nervoso centrale con una periferica esterna. Tali strumenti non impiegano le usuali vie efferenti coinvolte nella produzione di azioni quali nervi e muscoli, ma collegano l'attività cerebrale ad un computer che ne registra ed interpreta le variazioni, permettendo quindi di ripristinare in modo alternativo i collegamenti danneggiati e recuperare, almeno in parte, le funzioni perse. I risultati di numerosi studi dimostrano che i sistemi BCI possono consentire alle persone con gravi disabilità motorie di condividere le loro intenzioni con il mondo circostante e provano perciò il ruolo importante che esse sono in grado di svolgere in alcune fasi della loro vita.

Innovazioni nel campo dell'ElettroEncefalografia

Il funzionamento del cervello umano, organo responsabile di ogni nostra azione e pensiero, è sempre stato di grande interesse per la ricerca scientifica. Dopo aver compreso lo sviluppo dei potenziali elettrici da parte di nuclei neuronali in risposta a stimoli, si è riusciti a graficare il loro andamento con l'avvento dell'ElettroEncefaloGrafia (EEG). Tale tecnologia è entrata a far parte degli esami di routine per la ricerca di neuropsicologia e di interesse clinico, poiché permette di diagnosticare e discriminare i vari tipi di epilessia, la presenza di traumi cranici e altre patologie del sistema nervoso centrale. Purtroppo presenta svariati difetti: il segnale è affetto da disturbi e richiede un'adeguata elaborazione tramite filtraggio e amplificazione, rimanendo comunque sensibile a disomogeneità dei tessuti biologici e rendendo difficoltoso il riconoscimento delle sorgenti del segnale che si sono attivate durante l'esame (il cosiddetto problema inverso). Negli ultimi decenni la ricerca ha portato allo sviluppo di nuove tecniche d'indagine, di particolare interesse sono la ElettroEncefaloGrafia ad Alta Risoluzione (HREEG) e la MagnetoEncefaloGrafia (MEG). L'HREEG impiega un maggior numero di elettrodi (fino a 256) e l'appoggio di accurati modelli matematici per approssimare la distribuzione di potenziale elettrico sulla cute del soggetto, garantendo una migliore risoluzione spaziale e maggior sicurezza nel riscontro delle sorgenti neuronali. Il progresso nel campo dei superconduttori ha reso possibile lo sviluppo della MEG, che è in grado di registrare i deboli campi magnetici prodotti dai segnali elettrici corticali, dando informazioni immuni dalle disomogeneità dei tessuti e andando ad affiancare l'EEG nella ricerca scientifica. Queste nuove tecnologie hanno aperto nuovi campi di sviluppo, più importante la possibilità di comandare protesi e dispositivi tramite sforzo mentale (Brain Computer Interface). Il futuro lascia ben sperare per ulteriori innovazioni.

MRI-based brain volumetry in chronic whiplash patients: no evidence for traumatic brain injury

INTRODUCTION: Cognitive complaints, such as poor concentration and memory deficits, are frequent after whiplash injury and play an important role in disability. The origin of these complaints is discussed controversially. Some authors postulate brain lesions as a consequence of whiplash injuries. Potential diffuse axonal injury (DAI) with subsequent atrophy of the brain and ventricular expansion is of particular interest as focal brain lesions have not been documented so far in whiplash injury. OBJECTIVE: To investigate whether traumatic brain injury can be identified using a magnetic resonance (MR)-based quantitative analysis of normalized ventricle-brain ratios (VBR) in chronic whiplash patients with subjective cognitive impairment that cannot be objectively confirmed by neuropsychological testing. MATERIALS AND METHODS: MR examination was performed in 21 patients with whiplash injury and symptom persistence for 9 months on average and in 18 matched healthy controls. Conventional MR imaging (MRI) was used to assess the volumes of grey and white matter and of ventricles. The normalized VBR was calculated. RESULTS: The values of normalized VBR did not differ in whiplash patients when compared with that in healthy controls (F = 0.216, P = 0.645). CONCLUSIONS: This study does not support loss of brain tissue following whiplash injury as measured by VBR. On this basis, traumatic brain injury with subsequent DAI does not seem to be the underlying mechanism for persistent concentration and memory deficits that are subjectively reported but not objectively verifiable as neuropsychological deficits.

Code and readme of a MATLAB-based graphical user interface program for computing functionals of the geopotential

We present a novel graphical user interface program GrafLab (GRAvity Field LABoratory) for spherical harmonic synthesis (SHS) created in MATLAB®. This program allows to comfortably compute 38 various functionals of the geopotential up to ultra-high degrees and orders of spherical harmonic expansion. For the most difficult part of the SHS, namely the evaluation of the fully normalized associated Legendre functions (fnALFs), we used three different approaches according to required maximum degree: (i) the standard forward column method (up to maximum degree 1800, in some cases up to degree 2190); (ii) the modified forward column method combined with Horner's scheme (up to maximum degree 2700); (iii) the extended-range arithmetic (up to an arbitrary maximum degree). For the maximum degree 2190, the SHS with fnALFs evaluated using the extended-range arithmetic approach takes only approximately 2-3 times longer than its standard arithmetic counterpart, i.e. the standard forward column method. In the GrafLab, the functionals of the geopotential can be evaluated on a regular grid or point-wise, while the input coordinates can either be read from a data file or entered manually. For the computation on a regular grid we decided to apply the lumped coefficients approach due to significant time-efficiency of this method. Furthermore, if a full variance-covariance matrix of spherical harmonic coefficients is available, it is possible to compute the commission errors of the functionals. When computing on a regular grid, the output functionals or their commission errors may be depicted on a map using automatically selected cartographic projection.

Development of Brain-Computer Interfaces using Evolvable Hardware

Brain-Computer Interfaces are usually tackled from a medical point of view, correlating observed phenomena to physical facts known about the brain. Existing methods of classification lie in the application of deterministic algorithms and depend on certain degree of knowledge about the underlying phenomena so as to process data. In this demo, different architectures for an evolvable hardware classifier implemented on an FPGA are proposed, in line with the objective of generalizing evolutionary algorithms regardless of the application.

Development of the DORI-TBI: An Investigation to Develop a New Screening Measure to Determine Consultation with a School-Based Brain Injury Resource Team

Brain injury is the leading cause of disability and death in children in the United States. Student re-entry into the school setting following a traumatic brain injury is crucial to student success. Multidisciplinary teams within the school district comprised of individuals with expertise in brain injury are ideal in implementing student specific treatment plans given their specialized training and wide range of expertise addressing student needs. Therefore, the purpose of this study is to develop and initially validate a quantitative instrument that school personnel can use to determine if a student, identified as having a traumatic brain injury, will benefit from district-level consultation from a brain injury team. Three studies were designed to investigate the research questions. In study one, the planning and construction of the DORI-TBI was completed. Study two addressed the content validity of the DORI-TBI through a comparison analysis with other referral forms, content review with experts in the field of TBI, and cognitive interviews with professionals to test the usability of the new screening tool. In study three, a field administration was conducted using vignettes to measure construct validity. Results produced a valid and reliable new screening instrument that can aid school-based teams to more efficiently utilize district level consultation with a brain injury support team.

An organizational perspective on goal setting in community-based brain injury rehabilitation

Purpose. To use a taxonomy of goal content, developed in community-based brain injury rehabilitation to examine and compare the content of goals set within two different service settings; and to further examine the potential of the taxonomy to be a reliable and comprehensive framework for classifying goals. Method. Qualitative analysis and categorization of 1492 goal statements extracted from a community-based brain injury rehabilitation service over two time periods (1996-97, 1998-99), and cross-organizational comparison of ratings of goal classifications using a random sample of 100 goal statements drawn from this data set and the original 1765 goal statements used in developing the taxonomy. Results. Application of the taxonomy beyond the original service setting in which it was developed indicated a strong inter-rater reliability, with a high test-retest agreement reported over time. For both services, a small number of categories accounted for a substantial proportion of goals set within the two time periods, while considerable change was evident in goals between the two periods for one service. Further, both placed emphasis on individually focused goals rather than relationship or family-related goals. Conclusion. The taxonomy provides a reliable means for classifying goals and is a useful tool for exploration of the multiple influences on goal setting. Further application of the taxonomy to examine the relative influence on goal setting of client factors versus a range of organizational factors would be beneficial.

A unification-based natural language interface to a database.

An implementation of a Lexical Functional Grammar (LFG) natural language front-end to a database is presented, and its capabilities demonstrated by reference to a set of queries used in the Chat-80 system. The potential of LFG for such applications is explored. Other grammars previously used for this purpose are briefly reviewed and contrasted with LFG. The basic LFG formalism is fully described, both as to its syntax and semantics, and the deficiencies of the latter for database access application shown. Other current LFG implementations are reviewed and contrasted with the LFG implementation developed here specifically for database access. The implementation described here allows a natural language interface to a specific Prolog database to be produced from a set of grammar rule and lexical specifications in an LFG-like notation. In addition to this the interface system uses a simple database description to compile metadata about the database for later use in planning the execution of queries. Extensions to LFG's semantic component are shown to be necessary to produce a satisfactory functional analysis and semantic output for querying a database. A diverse set of natural language constructs are analysed using LFG and the derivation of Prolog queries from the F-structure output of LFG is illustrated. The functional description produced from LFG is proposed as sufficient for resolving many problems of quantification and attachment.

Análise de sinais cerebrais utilizando árvores de decisão

Este trabalho propõe um estudo de sinais cerebrais aplicados em sistemas BCI (Brain-Computer Interface - Interfaces Cérebro Computador), através do uso de Árvores de Decisão e da análise dessas árvores com base nas Neurociências. Para realizar o tratamento dos dados são necessárias 5 fases: aquisição de dados, pré-processamento, extração de características, classificação e validação. Neste trabalho, todas as fases são contempladas. Contudo, enfatiza-se as fases de classificação e de validação. Na classificação utiliza-se a técnica de Inteligência Artificial denominada Árvores de Decisão. Essa técnica é reconhecida na literatura como uma das formas mais simples e bem sucedidas de algoritmos de aprendizagem. Já a fase de validação é realizada nos estudos baseados na Neurociência, que é um conjunto das disciplinas que estudam o sistema nervoso, sua estrutura, seu desenvolvimento, funcionamento, evolução, relação com o comportamento e a mente, e também suas alterações. Os resultados obtidos neste trabalho são promissores, mesmo sendo iniciais, visto que podem melhor explicar, com a utilização de uma forma automática, alguns processos cerebrais.