During this master-thesis a robot controller designed for low-throughput and noisy EEG-data of a Brain Computer Interface (BCI) is implemented. The hypothesis of this master-thesis state that it is possible to design a modular and platform independent BCI-based controller for a mobile robot, which regulates the autonomy of the robot as a function of the user’s will to control. The BCI design is thoroughly described, including both the design choices regarding used brain activity signals and the pre- and post-processing of EEG data. The robot controller is experimentally tested by completing a set of missions in a simulated environment. Both quantitative and qualitative data is derived from the experimental test setup and used to evaluate the controller performance with different levels of induced noise. Additional to the robot control performance result, an offline validation of the BCI performance is depicted. Strength and weaknesses of the system design is presented based on the acquired result, and suggested solutions to improve the over-all performance is given. The produced result show that using the developed controller is a feasible approach for reliable and intuitive manoeuvring of a telepresence robot.

Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.

Brain computer interface (BCI) systems measure brain signal and translate it into control commands in an attempt to mimic specific human thinking activities. In recent years, many researchers have shown their interests in BCI systems, which has resulted in many experiments and applications. However, most methods are just based on a specific selected dataset or a typical feature. As a result, there are questions about whether some methods generalise well on other datasets. Therefore, the major motivation of this thesis is to compare various features and classifiers described in the literature. Pattern recognition is considered as the core part of a BCI system in our research. In this thesis, a number of different features and classifiers are compared in terms of classification accuracy and computation time. The studied features are: time series waveform, autoregressive (AR) components, spectral components; these are used with different classifiers: such as template matching, nearest neighbour, linear discriminant analysis (LDA), Bayesian statistical and fuzzy logic decision classifiers. In order to assess and compare these different features and classifiers, an extensive investigation was carried out on a public dataset (imagined left or right hand movement) from an international BCI competition and the results are reported in this thesis. The classification was done in a continuous fashion, to match a real time application. In this process, the average and best accuracy, as well as the computation time, were analysed and compared. The results showed that most classifiers achieved very high accuracies and short computation times for most features. A BCI experiment based on imagined left or right hand movement was carried out at the University of Adelaide and some investigations on the data from this experiment are discussed. The result shows that the selected classifiers can work well with this new dataset without much additional preprocessing or modifications. Finally, this thesis culminates with some conclusions based on our research, and discusses some further potential work. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1415396 / Thesis (M.Eng.Sc.) - University of Adelaide, School of Electrical and Electronic Engineering, 2010

Brain-computer music interfacing (BCMI) presents a novel approach to music making, as it requires only the brainwaves of a user to control musical parameters. This presents immediate benefits for users with motor disabilities that may otherwise prevent them from engaging in traditional musical activities such as composition, performance or collaboration with other musicians. BCMI systems with active control, where a user can make cognitive choices that are detected within brain signals, provide a platform for developing new approaches towards accomplishing these activities. BCMI systems that use passive control present an interesting alternate to active control, where control over music is accomplished by harnessing brainwave patterns that are associated with subconscious mental states. Recent developments in brainwave measuring technologies, in particular electroencephalography (EEG), have made brainwave interaction with computer systems more affordable and accessible and the time is ripe for research into the potential such technologies can offer for creative applications for users of all abilities. This thesis presents an account of BCMI development that investigates methods of active, passive and hybrid (multiple control methods) control that include control over electronic music, acoustic instrumental music, multi-brain systems and combining methods of brainwave control. In practice there are many obstacles associated with detecting useful brainwave signals, in particular when scaling systems otherwise designed for medical studies for use outside of laboratory settings. Two key areas are addressed throughout this thesis. Firstly, improving the accuracy of meaningful brain signal detection in BCMI, and secondly, exploring the creativity available in user control through ways in which brainwaves can be mapped to musical features. Six BCMIs are presented in this thesis, each with the objective of exploring a unique aspect of user control. Four of these systems are designed for live BCMI concert performance, one evaluates a proof-of-concept through end-user testing and one is designed as a musical composition tool. The thesis begins by exploring the field of brainwave detection and control and identifies the steady-state visually evoked potential (SSVEP) method of eliciting brainwave control as a suitable technique for use in BCMI. In an attempt to improve signal accuracy of the SSVEP technique a new modular hardware unit is presented that provides accurate SSVEP stimuli, suitable for live music performance. Experimental data confirms the performance of the unit in tests across three different EEG hardware platforms. Results across 11 users indicate that a mean accuracy of 96% and an average response time of 3.88 seconds are attainable with the system. These results contribute to the development of the BCMI for Activating Memory, a multi-user system. Once a stable SSVEP platform is developed, control is extended through the integration of two more brainwave control techniques: affective (emotional) state detection and motor imagery response. In order to ascertain the suitability of the former an experiment confirms the accuracy of EEG when measuring affective states in response to music in a pilot study. This thesis demonstrates how a range of brainwave detection methods can be used for creative control in musical applications. Video and audio excerpts of BCMI pieces are also included in the Appendices.

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Interface cerveau-machine pour compenser les déficits moteurs chez des patients ayant des troubles moteurs, avec des implantations chroniques d'électrodes corticales. Etude expérimentale sur animaux. Une interface cerveau-machine (ICM) est définie comme un système de communication qui permet à l'activité cérébrale seule de contrôler des effecteurs externes. L'objectif immédiat des ICM est de fournir des capacités de communication aux personnes gravement handicapées qui sont totalement paralysées par des troubles neuromusculaires, tels que la sclérose latérale amyotrophique, l'accident vasculaire cérébral ou une lésion de la moelle épinière. Des résultats prometteurs (des patients pilotent un joystick grâce à la modulation de leur activité corticale) permettre d'accroître l'espoir dans de futures applications d'ICM avec une matrice de microélectrodes implantées chroniquement à la surface du cortex. Des expériences récentes ont démontré la capacité d'un tétraplégique à contrôler un bras robotisé. Ce travail de thèse contribue aux études précliniques, réalisées en parallèle du développement technique afin de fournir la validation du protocole expérimental chez l'homme par étapes successives. Il permet de développer un dispositif d'enregistrement ElectroCorticoGramme (ECoG) chez des rats, pour l'implanter chez ces animaux et enregistrer leur activité ECoG lors d'expériences comportementales pour contrôler un effecteur externe. Deux types d'études en ligne ont été effectués: le contrôle du distributeur directement par l'activité corticale ou par la combinaison de la tâche motrice (appuyer sur la pédale) et la détection de la signature. Dans les études de contrôle direct par la détection, la Performance Générale (PG) de notre ICM a été de 21,01% ± 4,33 (10 animaux 69 expériences), mais le nombre d'appuis par minute est tombé à 0,57±0,47 rendant plus difficile l'interprétation de ces résultats. C'est pourquoi les expériences, plus complexes, nécessitant l'activation du levier et la détection de signature ont été réalisés. La PG, dans ce cas, est de 37,76% ± 9,64 avec un nombre d'appuis qui a augmenté à 3,24 ± 0,7. La comparaison avec une détection aléatoire nous a permis d'être sûr que ces résultats ne sont pas aléatoires (environ 25-30 fois plus que l'analyse aléatoire). L'une des caractéristiques la plus intéressante de ces expériences est que la zone qui semble en évidence concernée par l'exécution de la tâche motrice est la région du cervelet et non la zone motrice et sensori-motrice, zones qui étaient attendues, comme pour les humains. Un aspect de notre étude sur la neuroplasticité a été de démontrer que la signature, une fois identifiée sur le cervelet, peut être détectée en temps réel dans d'autres régions du cerveau. Nos résultats ont montré une PG de 15,16% ± 3,75 dans 97 expériences faites sur 8 rats. Ces résultats ont montré que l'activité cérébrale en corrélation avec la tâche comportementale, identifiée en premier lieu dans le cervelet, peut être détectée dans une zone différente du cerveau. La caractéristique principale de ce travail de thèse est la démonstration que l'activité neuronale enregistrée en continu au niveau d'une électrode corticale unique peut être efficacement utilisée pour piloter un effecteur avec un degré de liberté, au cours d'expériences longue durant jusqu'à une heure, avec un animal libre de ses mouvements capable de prendre des décisions de manière aléatoire sans indication. Ce travail est une étape déterminante, un premier pas, vers un programme plus vaste visant à fournir un certain niveau de mobilité pour des jeunes patients tétraplégiques. / Brain computer interface with chronic cortical electrode arrays for motor deficit compensation in motor disabled patients. Experimental study in rodents. A brain-computer interface (BCI) is currently defined as a hardware and software communication system that permits cerebral activity alone to control external devices. The immediate goal of BCI research is to provide communication capabilities to severely disabled people who are totally paralyzed or ‘locked in' by neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Promising results (patients piloting a joystick through modulation of their cortical activity) increase the hope of BCI with an array of microelectrodes chronically implanted at the cortex's surface, which doesn't exist yet. Recent experiments demonstrated the capacity for a tetraplegic to control a robotic arm. This PhD work contributes to preclinical studies, performed in parallel of technical development to provide validation of the human experimental protocol in successive steps. It contributes to develop ECoG recording device for rats, to implant them in the corresponding animals and record their ECoG activity during freely moving behavioural experiments to control an external effector. Two kinds of on-line studies have been done: the control of the dispenser directly by cortical activity or by the combination of motor task (push the lever) and detection of the signature. In studies of direct control by the detection the Overall Performance (OP) was 21,01%±4,33 (10 animals 69 experiments) but the number of push per minute fell to 0,57±0,47 making more difficult the interpretation of these results. That's why the experiments, more complicated, requiring both lever activation and signature detection have been realized. The OP, in this case, is 37,76%±9,64 with a number of push which increased back to 3,24±0,7. The comparison with random detection permitted us to be sure that these results are not random (around 25-30 fold more than random analysis). One of the most intriguing features of these experiments is that the area which seems prominently concerned by the execution of the motor task is the cerebellar area and not the central, motor and sensorimotor, areas which would be expected, as in human beings. An aspect of our neuroplasticity study has been to demonstrate that the signature, once identified on cerebellum, can be detected in real-time in other areas of the brain. Our results showed an OP of 15,16%±3,75 in 97 experiments done on 8 rats. These results showed that brain activities correlated with behavioural task identified firstly in cerebellum can be detected in a different area of the brain. The main feature of this report is the demonstration that neural activity continuously recorded at the level of one single cortical electrode can be efficiently used to pilot an effector with one degree of freedom, during experiments up to 1 hour, in a freely moving individual making decisions in a random unsupervised manner. This work is a determining first step towards a larger program aiming at providing a certain level of mobility to young cervical spinal-cord injured patients with tetraplegia.

There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software to determine the most significant frequency and electrode combination. This improved method found increased accuracy in classifying cases as either active or idle states. Future directions could be using multiple features for classification and BCI control, or exploiting the difference between ERD and ERS, though for either of these a more advanced algorithm would be required.

It is widely understood that neurons within the brain produce electrical activity, and electroencephalography—a technique used to measure biopotentials with electrodes placed upon the scalp—has been used to observe it. Today, scientists and engineers work to interface these electrical neural signals with computers and machines through the field of Brain-Computer Interfacing (BCI). BCI systems have the potential to greatly improve the quality of life of physically handicapped individuals by replacing or assisting missing or debilitated motor functions. This research thus aims to further improve the efficacy of the BCI based assistive technologies used to aid physically disabled individuals. This study deals with the testing and modification of a BCI system that uses the alpha and beta bands to detect motor intention by weighing online EEG output against a calibrated threshold.

Thesis advisor: Robert Signorile / It has long been known that as neurons fire within the brain they produce measurable electrical activity. Electroencephalography (EEG) is the measurement and recording of these electrical signals using sensors arrayed across the scalp. Though there is copious research in using EEG technology in the fields of neuroscience and cognitive psychology, it is only recently that the possibility of utilizing EEG measurements as inputs in the control of computers has emerged. The idea of Brain-Computer Interfaces (BCIs) which allow the control of devices using brain signals evolved from the realm of science fiction to simple devices that currently exist. BCIs naturally present themselves to many extremely useful applications including prosthetic devices, restoring or aiding in communication and hearing, military applications, video gaming and virtual reality, and robotic control, and have the possibility of significantly improving the quality of life of many disabled individuals. However, current BCIs suffer from many problems including inaccuracies, delays between thought, detection, and action, exorbitant costs, and invasive surgeries. The purpose of this research is to examine the Emotiv EPOC© System as a cost-effective gateway to non-invasive portable EEG measurements and utilize it to build a thought-based BCI to control the Parallax Scribbler® robot. This research furthers the analysis of the current pros and cons of EEG technology as it pertains to BCIs and offers a glimpse of the future potential capabilities of BCI systems. / Thesis (BA) — Boston College, 2010. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Computer Science Honors Program. / Discipline: Computer Science.

abstract: Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can be either penetrating or non-penetrating. The non-penetrating subset sits on the tissue surface without puncturing the parenchyma and is known to engender minimal tissue response and less damage than the penetrating counterpart, improving long term viability in vivo. Provided non-penetrating microelectrodes can consistently evoke perception and maintain a localized region of activation, non-penetrating micro-electrodes may provide an ideal platform for a high performing neural prosthesis; this dissertation explores their functional capacity. The scale at which non-penetrating electrode arrays can interface with cortex is evaluated in the context of extracting useful information. Articulate movements were decoded from surface microelectrode electrodes, and additional spatial analysis revealed unique signal content despite dense electrode spacing. With a basis for data extraction established, the focus shifts towards the information encoding half of neural interfaces. Finite element modeling was used to compare tissue recruitment under surface stimulation across electrode scales. Results indicated charge density-based metrics provide a reasonable approximation for current levels required to evoke a visual sensation and showed tissue recruitment increases exponentially with electrode diameter. Micro-scale electrodes (0.1 – 0.3 mm diameter) could sufficiently activate layers II/III in a model tuned to striate cortex while maintaining focal radii of activated tissue. In vivo testing proceeded in a nonhuman primate model. Stimulation consistently evoked visual percepts at safe current thresholds. Tracking perception thresholds across one year reflected stable values within minimal fluctuation. Modulating waveform parameters was found useful in reducing charge requirements to evoke perception. Pulse frequency and phase asymmetry were each used to reduce thresholds, improve charge efficiency, lower charge per phase – charge density metrics associated with tissue damage. No impairments to photic perception were observed during the course of the study, suggesting limited tissue damage from array implantation or electrically induced neurotoxicity. The subject consistently identified stimulation on closely spaced electrodes (2 mm center-to-center) as separate percepts, indicating sub-visual degree discrete resolution may be feasible with this platform. Although continued testing is necessary, preliminary results supports epicortical microelectrode arrays as a stable platform for interfacing with neural tissue and a viable option for bi-directional BCI applications. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2018