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A brain-computer interface (BCI) is a method of communication that utilizes the scalp recorded electroencephalogram (EEG). A BCI requires no movement, making it a viable communication option for people who are severely disabled. Most BCI research has focused on improving BCI technology through advances in signal processing and paradigmatic manipulations. Research has recently begun to examine the influence of psychosocial factors on BCI performance. Examining psychosocial factors may be particularly important for disabled people who have several co-morbidities. The purpose of the current study is to examine the hypothesis that participants will be more motivated in a free spelling paradigm than in a copy spelling paradigm. Participants completed copy- and freespelling tasks, order was counterbalanced. Motivation was measured after each task. Preliminary data suggests an increase in motivation after the second task regardless of which task was performed second. No differences were observed in performance accuracy between the two tasks.

No description available.

The human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain signals, translating and classifying various brain responses, and relaying them to external devices and potentially back to the body. Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, in this thesis we explore and develop algorithms that exploit the sparse characteristics of EEGs to classify these signals. Different feature vectors are extracted from EEG trials recorded by electrodes placed on the scalp. In this thesis, features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the EEG training signals for each class. This is used to classify the signals based on the pattern of their sparse representation using a minimum-residual decision rule. We first attempt to use all the available electrodes to verify the effectiveness of the proposed methods. To support real time BCI, the electrodes are reduced to those near the sensorimotor cortex which are believed to be crucial for motor preparation and imagination. In a second approach, we try to incorporate the effect of spatial correlation across the neighboring electrodes near the sensorimotor cortex. To this end, instead of considering one feature vector at a time, we use a collection of feature vectors simultaneously to find the joint sparse representation of these vectors. Although we were not able to see much improvement with respect to the first approach, we envision that such improvements could be achieved using more refined models that can be subject of future works. The performance of the proposed approaches is evaluated using different features, including wavelet coefficients, energy of the signals in different frequency sub-bands, and also entropy of the signals. The results obtained from real data demonstrate that the combination of energy and entropy features enable efficient classification of motor imagery EEG trials related to hand and foot movements. This underscores the relevance of the energies and their distribution in different frequency sub-bands for classifying movement-specific EEG patterns in agreement with the existence of different levels within the alpha band. The proposed approach is also shown to outperform the state-of-the-art algorithm that uses feature vectors obtained from energies of multiple spatial projections.

The focus of this thesis was to measure the intracellular voltage of a living neural cell using a silicon transistor. The coupling of neurological tissues with silicon devices is of interest to the fields of neurology, neuroscience, electrophysiology and cellular biology. In previous work by Peter Fromherz, single neurons were successfully coupled to transistors [1]. This thesis aims to show proof of concept of the fabrication of a simple neuro-silicon interface using wafer processing methods currently available at Cal Poly. The types of transistors and cells used, the methods for dissecting and preparing the cells, the electrophysiology methods for validating the experiments, and portions of the design of the junction were based on Fromherz’s 1991 work. Other aspects were revised to be compatible with technologies available at Cal Poly. Leech Retzius cells were isolated and cultured from Hirudo Medicinalis and joined to the gate oxide of a P-channel field effect transistor using SU-8 photoresist wells treated with poly-l-lysine. Transistors were operated in strong inversion and source-drain currentfluctuations were observed that correlated with action potentials of the current clamped Retzius cell. Further work is needed to develop better junctions that can reliably couple action potentials. This work lays a foundation for neuro-silicon interface fabrication at Cal Poly.

No description available.

Human interfaces to computers continue to be an active area of research. The keyboard is considered the basic interface for editing control as well as text input. Problems of correct typing and typing speed have urged research for alternative means for keyboard replacement, or at least "resizing" its monopoly. Pointing devices (e.g. a mouse) have been developed, and supporting software with icons is now widely used. Two other means are being developed and operationally tested, namely, the pen for handwriting text, commands and drawings, and spoken language interface, which is the subject of this thesis. Human/computer interface is an interactive man-machine communication facility that enjoys the following advantages. • High input speed: some experiments reveal that the rate of information input by speech is three times faster than keyboard input and eight times faster than inputting characters by hand. • No training needed: because the generation of speech is a very natural human action, it requires no special training. • Parallel processing with other information: production of speech works quite well in conjunction with gestures of hands and feet for visual perception of information. • Simple and economical input sensor: microphones are inexpensive and are readily available. • Coping with handicaps: these interfaces can be used in unusual circumstances of darkness, blindness, or other visual handicap. This dissertation presents a design of a Human Computer Interface (HCI) system that can be trained to work with an individual speaker. A new approach is introduced to extract key voice features, called Median Linear Predictive Coding (MLPC). MLPC reduces the HCI calculation time and gives an improved recognition rate. This design eliminated the typical Multi-layer Perceptron (MLP) problems of complexity growth with vocabulary size, the large training times required and the need for complete re-training whenever the vocabulary is extended. A novel modular neural network architecture, called a Pyramidal Modular Neural Network (PMNN), is introduced for recursive speech identification. In addition, many other system algorithms/components, such as speech endpoint detection, automatic noise thresholding, etc., must be tailored correctly in order to achieve high recognition accuracy. / Ph. D.

Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. The novel algorithms were found to improve classification accuracy as well as reduce the computational costs of Riemannian classification methods for binary, synchronous classification on BCI competition IV dataset 2a. / Master of Science

Neuroninė kompiuterio sąsaja – tai alternatyvus būdas valdyti kompiuterį nenaudojant rankų. Ji gali būti apibrėžta, kaip komunikavimo sistema, kuri leidžia valdyti kompiuterį ar kitą skaitmeninį įrenginį, naudojant nervinės kilmės fiziologinius signalus. Pagrindinė neuroninės kompiuterio sąsajos taikymo sritis yra neįgaliesiems skirti įrenginiai. Tai ne tik specifiniai įrenginiai, tokie, kaip galūnių protezai, tačiau ir kompiuteriai su papildoma aparatine ir programine įranga, kuri leidžia žmonėms, nevaldantiems rankų, valdyti kompiuterį. Taip pat išmanieji invalido vežimėliai, kuriuos galima vairuoti nenaudojant rankų judesių. Šiame darbe analizuojama neuroninė kompiuterio sąsaja, skirta vežimėlio tipo roboto valdymui. Pateikiama mūsų sukurta neuroninės kompiuterio sąsajos sistema, gebanti nuskaityti žmogaus EEG ir galvos raumenų EMG signalus, juos apdoroti, klasifikuoti ir jų pagalba valdyti vežimėlio tipo robotą. Taip pat pateikiamas trijų komandų vežimėlio tipo roboto valdymo per galvos paviršinio EMG signalo lygį metodas. Pateikiami roboto valdymo taikant šį metodą tikslumo eksperimentai ir jų rezultatai. / Neural computer interface is alternative way to control computer without hands. It is defined as a communication system which allows user control computer or any other digital device using neural breed physiological signal. The main application of neural computer interface is various devices for people with disabilities. For example, electronic prosthetic limbs, PC‘s with additional hardware and software which allows people with motor disabilities to control PC or intelligent wheelchairs. In this work we are analyzing neural computer interface applied for robot control. The author presents neural computer interface system which allows to read EEG and head surface EMG signals, pre-process the signals, classify the signals and control Arduino 4WD robot. We also propose approach to control robot with head surface EMG signal amplitude using 3 control commands. Robot control research using proposed approach is presented.

Syftet med uppsatsen är att ge en uppdaterad översikt av området BCI (Brain Computer Interface) och undersöka vad som hänt sedan begreppet introducerades i forskningssammanhang; vilka praktiska resultat forskningen lett till och vilka tillämpningar som tillkommit. Metoden som företrädesvis används är litteraturstudie som tecknar bakgrund och enkät. Därefter följer en diskussion där utmaningar för framtiden, potential och tillämpningar i BCI-tekniken behandlas utifrån ett neuropsykologiskt perspektiv. Kommer BCI-tekniken att implementeras på samma sätt som radio, TV och telekommunikationer i samhället och vilka etiska och tekniska problem finns idag. För att skildra allmänhetens uppfattning om BCI genomfördes en webbaserad enkätundersökning (survey) i form av pilotstudie (n=32) som syftar till att ge en indikation på attityder och hur allmänhetens opinion med avseende på tillämpningar i samtiden och jämförelser med avseende på teknisk bakgrund.