Studien implementerade ett hjärna-dator-gränssnitt med hjälp av EEG-instrumentet MindWave Mobile Headset. Vi undersökte om det var möjligt att utföra fyra operationer genom att använda tankemönster. Fyra försökspersoner deltog i studien. Deras uppgift var att tänka i två tankemönster i följd som resulterade i en operation. EEG-signalen förbehandlas så att en mönsterigenkänningsmetod (k-NN) lättare kunde urskilja två tankemönster ur signalen. Denna undersökning har till vår vetskap inte tidigare utförts och är därmed kunskapsluckan vi ämnar fylla. Att fylla denna kunskapslucka är av intresse för bland annat användargrupperna: rörelsehindrade, spelintresserade och Virtual Reality-användare. Vi tog fram en modell som modellerade det bästa möjliga utfallet av metodiken i föreliggande studie. Undersökningens resultat kunde inte användas för att göra slutsatser angående frågeställningen då detta skulle vara att post hoc-teoretisera. I modellen visades dock tre av fyra operationer vara genomförbara, med en indikation om att även den fjärde var möjlig att utföra. Resultatet indikerar att det finns anledning att utföra en fortsatt studie. Den föreslagna fortsatta studien bör innefatta nya mätningar som testas av modellen för att fullt ut besvara problemformuleringen. / This study implements a Brain-Computer-Interface using the EEG-instrument MindWave Mobile Headset. We studied the feasibility of performing four operations using thought patterns. Four test subjects participated in the study. Their task was to think in two subsequent thought patterns that resulted in an operation. The EEG-signal was pre-processed in such a way that a pattern recognition algorithm (k-NN) more easily could recognize two thought patterns in the signal. This study has to our knowledge not been done before and thus aims to fill this lack of knowledge in the scientific community. User groups that have an interest in filling this gap are, amongst others; disabled people, gamers, and Virtual Reality users. We created a model that modeled the best possible outcome of the method used in this study. Conclusions drawn from the result can not be used to fully answer the problem statement, since it would be to post hoc-theorize. However, three out of four operations were possible to perform in the model, with an indication that the fourth also was possible to perform. These results indicate that there are grounds to continue this study. The proposed continued study should include new measurements that are tested by the model to determine if it is feasible to distinguish all four operations.

Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system. Access control mechanisms rely on a number of authenticators, including “what you know”, “what you have”, and “what you are”. The “what you are” authenticator, formally known as a biometrics authenticator, is increasingly gaining acceptance. It uses an individual’s unique features such as fingerprints and facial images to properly authenticate users. An emerging approach in physiological biometrics is cognitive biometrics, which measures brain’s response to stimuli. These stimuli can be measured by a number of devices, including EEG systems. This work shows an approach to authenticate users interacting with their computational devices through the use of EEG devices. The results demonstrate the feasibility of using a unique hard-to-forge trait as an absolute biometrics authenticator by exploiting the signals generated by different areas of the brain when exposed to visual stimuli. The outcome of this research highlights the importance of the prefrontal cortex and temporal lobes to capture unique responses to images that trigger emotional responses. Additionally, the utilization of logarithmic band power processing combined with LDA as the machine learning algorithm provides higher accuracy when compared against common spatial patterns or windowed means processing in combination with GMM and SVM machine learning algorithms. These results continue to validate the value of logarithmic band power processing and LDA when applied to oscillatory processes.

Locked-in syndrome (LIS) is a condition characterized by total or near-total paralysis with preserved cognitive and somatosensory function. For the locked-in, brain-machine interfaces (BMI) provide a level of restored communication and interaction with the world, though this technology has not reached its fullest potential. Several streams of research explore improving BMI performance but very little attention has been given to the paradigms implemented and the resulting constraints imposed on the users. Learning new mental tasks, constant use of external stimuli, and high attentional and cognitive processing loads are common demands imposed by BMI. These paradigm constraints negatively affect BMI performance by locked-in patients. In an effort to develop simpler and more reliable BMI for those suffering from LIS, this dissertation explores using error-related potentials, the neural correlates of error awareness, as an access pathway for adaptive decoding and direct volitional control. In the first part of this thesis we characterize error-related local field potentials (eLFP) and implement a real-time decoder error detection (DED) system using eLFP while non-human primates controlled a saccade BMI. Our results show specific traits in the eLFP that bridge current knowledge of non-BMI evoked error-related potentials with error-potentials evoked during BMI control. Moreover, we successfully perform real-time DED via, to our knowledge, the first real-time LFP-based DED system integrated into an invasive BMI, demonstrating that error-based adaptive decoding can become a standard feature in BMI design. In the second part of this thesis, we focus on employing electroencephalography error-related potentials (ErrP) for direct volitional control. These signals were employed as an indicator of the user’s intentions under a closed-loop binary-choice robot reaching task. Although this approach is technically challenging, our results demonstrate that ErrP can be used for direct control via binary selection and, given the appropriate levels of task engagement and agency, single-trial closed-loop ErrP decoding is possible. Taken together, this work contributes to a deeper understanding of error-related potentials evoked during BMI control and opens new avenues of research for employing ErrP as a direct control signal for BMI. For the locked-in community, these advancements could foster the development of real-time intuitive brain-machine control.

This thesis uses one of Brain Computer Interface (BCI) products, NeuroSky headset, to design a prototype model to control window blind by using headset’s single channel electrode. Seven volunteers performed eight different exercises while the signal from the headset was recorded. The dataset was analyzed, and exercises with strongest power spectral density (PSD) were chosen to continue to work with. Matlabs spectrogram function was used to divide the signal in time segments, which were 0.25 seconds. One segment from each of these eight exercises was taken to form different combinations which were later classified.The classification result, while using two of proposed exercises (tasks) was successful with 97.0% accuracy computed by Nearest Neighbor classifier. Still, we continued to investigate if we could use three or four thoughts to create three or four commands. The result presented lower classification accuracy when using either 3 or 4 command thoughts with performance accuracy of 92% and 76% respectively.Thus, two or three exercises can be used for constructing two or three different commands.

The main goal of this paper is to test the applicability of the device for neural computer control on a group of ten volunteers. In the next part of the paper author focuses on Electroencephalography and the conversion of analog neural signals from brain to digital form. Next chapter describes currently on the market available devices, which allow customers direct computer controlling with the usage of bio signal from brain. The device selected for the purposes of this paper (Emotiv Epoc) is more described in detail. The last goal is an attempt to predict the future development of this technology. The paper demonstrates applicability of this device in its current form for everyday work with Microsoft Project and presents users who are able to learn to control a computer with this device in just 980 minutes of training.

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2018-03-13T17:24:13Z No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-03-14T10:57:00Z (GMT) No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-03-14T10:57:00Z (GMT). No. of bitstreams: 2 Dissertação - Andrei Damian da Silva - 2018.pdf: 2168750 bytes, checksum: 4d47d811f294faae439470b427c48f3e (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper presents a new approach for the processing and classification of visual evoked potentials of steady state (SSVEP). It introduces a ensemble tree model that combines canonical correlation analysis data with methods based on estimation of power spectral density. The stimuli were created using LEDs, from 7.04 Hz to 38.46 Hz. Data were collected using the Texas Instruments ADS1299EEG-Fe and three electrodes. The tests were performed for different distances and light intensities to evaluate the performance of the algorithm under different conditions. In all, 22 participants were recruited, and the average classification was 99.1 ± 2.27% with fixed decision time of 1 second. / Este trabalho apresenta uma nova abordagem para o processamento e classificação de potenciais evocados visuais de estado estacionário (SSVEP). Este trabalho introduz um modelo de em aprendizagem por agrupamento de árvores de decisão que combina dados de análise da correlação canônica com métodos baseados na estimativa da densidade espectral de potência. Os estímulos foram criados utilizando LEDs, com frequência de 7.04 Hz até 38.46 Hz. Os dados foram coletados utilizando a placa ADS1299EEG-Fe da Texas Instruments e três eletrodos. Os testes foram realizados para diferentes distâncias e intensidades luminosas com o objetivo de avaliar o desempenho do algoritmo em condições diversas. Ao todo, 22 participantes foram recrutados e a taxa de acertos média foi de 99.1±2.27% com tempo de decisão fixo em 1 segundo.

A presente pesquisa é uma etnografia da recepção da publicidade do Banco Comercial e de Investimentos (BCI) exibida em espaços comerciais de televisão, rádio e outdoors em Moçambique a partir do ponto de vista dos clientes do banco. O estudo foi realizado na Cidade de Maputo, capital de Moçambique. Os seus resultados sugerem que a nova estratégia comunicacional do BCI apropria-se de “cultura local” (signos e elementos simbólicos tidos como moçambicanos) para se reposicionar no mercado bancário local. Como corolário de “apropriação” de elementos identitarios “locais”, na imaginação e nos discursos dos interlocutores da pesquisa, o BCI é entendido como um banco privado moçambicano, um banco “daqui”, um banco que respeita a “cultura moçambicana”. Os resultados da pesquisa revelam, também, que a localidade presente nos anúncios do BCI, para os clientes do banco, figura como um pretexto para a reinvenção da nação moçambicana numa época em que o país é desafiado pelo capitalismo neoliberal cada vez mais mundializado. / This research is ethnography of bank customers’ perception of advertisement of the Commercial and Investment Bank (BCI) displayed in commercial spaces of television, radio and billboards in Mozambique. The study was conducted in Maputo, capital of Mozambique. Their results suggest that the new communication strategy of the BCI appropriates “local culture” (signs and symbolic elements taken as typically Mozambican) to reposition itself in the local banking market. As a corollary of this assimilation of elements of “local” identity in the imagination and in the discourses of the bank customers, BCI is understood by them as a Mozambican private bank, a bank “from here” (“um banco daqui”), and a bank with respects towards “Mozambican culture”. The survey concludes that for the bank customers the locality present in BCI advertisements figures as an argument for the reinvention of the Mozambican nation at a time when the increasingly globalizing country is challenged by neoliberal capitalism.

This research set out to develop a novel technique to decompose Electroencephalograph (EEG) signal into sets of constituent peaks in order to better describe the underlying nature of these signals. It began with the question; can a localised, single stimulation of sensory nervous tissue in the body be detected in the brain? Flash Visual Evoked Potential (VEP) tests were carried out on 3 participants by presenting a flash and recording the response in the occipital region of the cortex. By focussing on analysis techniques that retain a perspective across different domains � temporal (time), spectral (frequency/scale) and epoch (multiple events) � useful information was detected across multiple domains, which is not possible in single domain transform techniques. A comprehensive set of algorithms to decompose evoked potential data into sets of peaks was developed and tested using wavelet transform singularity detection methods. The set of extracted peaks then forms the basis for a subsequent clustering analysis which identifies sets of localised peaks that contribute the most towards the standard evoked response. The technique is quite novel as no closely similar work in research has been identified. New and valuable insights into the nature of an evoked potential signal have been identified. Although the number of stimuli required to calculate an Evoked Potential response has not been reduced, the amount of data contributing to this response has been effectively reduced by 75%. Therefore better examination of a small subset of the evoked potential data is possible. Furthermore, the response has been meaningfully decomposed into a small number (circa 20) of constituent peaksets that are defined in terms of the peak shape (time location, peak width and peak height) and number of peaks within the peak set. The question of why some evoked potential components appear more strongly than others is probed by this technique. Delineation between individual peak sizes and how often they occur is for the first time possible and this representation helps to provide an understanding of how particular evoked potentials components are made up. A major advantage of this techniques is the there are no pre-conditions, constraints or limitations. These techniques are highly relevant to all evoked potential modalities and other brain signal response applications � such as in brain-computer interface applications. Overall, a novel evoked potential technique has been described and tested. The results provide new insights into the nature of evoked potential peaks with potential application across various evoked potential modalities.

A key issue in understanding how the brain functions is the ability to correlate functional information with anatomical localisation. Functional information can be provided by a variety of techniques like positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) or transcranial magnetic stimulation (TMS). All these methods provide different, but complementary, information about the functional areas of the brain. PET and fMRI provide spatially accurate picture of brain regions involved in a given task. TMS permits to infer the contribution of the stimulated brain area to the task under investigation. EEG and MEG, which reflects brain activity directly, have temporal accuracy of the order of a millisecond. TMS, EEG and MEG are offset by their low spatial resolution. In this thesis, we propose two methods to improve the spatial accuracy of method based on TMS and EEG. The first part of this thesis presents an automatic method to improve the localisation of TMS points. The method enables real-time visualisation and registration of TMS evoked responses and MRI. A MF digitiser is used to sample approximately 200 points on the subject's head following a specific digitisation pattern. Registration is obtained by minimising the RMS point to surface distance, computed efficiently using the Euclidean distance transform. Functional maps are created from TMS evoked responses projected onto the brain surface previously segmented from MRI. The second part presents the possibilities to set up a brain-computer interface (BCI) based on reconstructed sources of EEG activity and the parameters to adjust. Reconstructed sources could improve the EEG spatial accuracy as well as add biophysical information on the origin of the signal. Both informations could improve the BCI classification step. Eight BCIs are built to enable comparison between electrode-based and reconstructed source-based BCIs. Tests on detection of laterality of upcoming hand movement demonstrate the interest of reconstructed sources.

Neural prostheses are being developed to provide motor capabilities to patients who suffer from motor-debilitating diseases and conditions. These brain-computer interfaces (BCI) will be controlled by activity from the brain and bypass damaged parts of the spinal cord or peripheral nervous system to re-establish volitional control of motor output. Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a BCI. No systematic analysis of the need for spike sorting has been carried out and little is known about the effects of spike sorting error on the ability of a BCI to decode intended motor commands. We developed a theoretical framework and a modelling environment to examine the effects of spike processing on the information available to a BCI decoder. Shannon information theory was applied to simulated neural data. Results demonstrated that reported amounts of spike sorting error reduce mutual information (MI) significantly in single-unit spike trains. These results prompted investigation into how much information is available in a cluster of pooled signals. Indirect information analysis revealed the conditions under which pooled multi-unit signals can maintain the MI that is available in the corresponding sorted signals and how the information loss grows with dissimilarity of MI among the pooled responses. To reveal the differences in non-sorted spike activity within the context of a BCI, we simulated responses of 4 neurons with the commonly observed and exploited cosine-tuning property and with varying levels of sorting error. Tolerances of angular tuning differences and spike sorting error were given for MI loss due to pooling under various conditions, such as cases of inter- and/or intra-electrode differences and combinations of various mean firing rates and tuning depths. These analyses revealed the degree to which mutual information loss due to pooling spike activity depended upon differences in tuning between pooled neurons and the amount of spike error introduced by sorting. The theoretical framework and computational tools presented in this dissertation will BCI system designers to make decisions with an understanding of the tradeoffs between a system with and without spike sorting. / Dissertation