PIN generation using EEG : a stability study

In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain–computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of this method is obviously its better fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e. to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable

Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings

Development of Electroencephalography based Brain Controlled Switch and Nerve Conduction Study Simulator Software

This thesis investigated the development of an EEG-based brain controlled switch and the design of a software for nerve conduction study. For EEG-based brain controlled switch, we proposed a novel paradigm for an online brain-controlled switch based on Event-Related Synchronizations (ERDs) following external sync signals. Furthermore, the ERD feature was enhanced by 3 event-related moving averages and the performance was tested online. Subjects were instructed to perform an intended motor task following an external sync signal in order to turn on a virtual switch. Meanwhile, the beta-band (16-20Hz) relative ERD power (ERD in reverse value order) of a single EEG Laplacian channel from primary motor area was calculated and filtered by 3 event-related moving average in real-time. The computer continuously monitored the filtered relative ERD power level until it exceeded a pre-set threshold selected based on the observations of ERD power range to turn on the virtual switch. Four right handed healthy volunteers participated in this study. The false positive rates encountered among the four subjects during the operation of the virtual switch were 0.8±0.4%, whereby the response time delay was 36.9±13.0s and the subjects required approximately 12.3±4.4 s of active urging time to perform repeated attempts in order to turn on the switch in the online experiments. The aim of nerve conduction simulator software design is to create software that can be used by nerve conduction simulator to serve as a medical simulator or education tool to train novice physicians for nerve conduction study test. The real response waveform of 10 different upper limb nerves in conduction studies were obtained from the equipment used in real patient studies. A waveform generation model was built to generalize the response waveform near the standard stimulus site within study interest region based on the extracted waveforms and normal reference parameters of each study and stimulus site coordinates. Finally, based on the model, a software interface was created to simulate 10 different nerve conduction studies of the upper limb with 9 pathological conditions

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements

Reconocimiento del habla silenciosa con señales electroencefalográficas (EEG) para interfaces cerebro-computador

Las interfaces cerebro computador tienen relevancia médica en el tratamiento de individuos que sufren de parálisis motora o amputaciones de miembro superior o miembro inferior; sin embargo, la dificultad para extraer y procesar con exactitud, particularmente las señales cerebrales de habla silenciosa, limita en gran medida su aplicación. Esta tesis presenta dos novedosos sistemas de procesamiento de señales basados en electroencefalografía con la habilidad de clasificar vocales y sílabas con habla silenciosa. Una de las metodologías se basa en las características de la entropía de la información con la dimensión de regularización, y el otro en las características de datos funcionales en el espacio de Hilbert $L^2$, utilizando los datos de la posición de los electrodos. Dentro de las ventajas de los métodos desarrollados en comparación con otros métodos de BCI, pueden establecerse los siguientes: no requieren de procesos de entrenamiento dispendiosos como en el caso de la imaginería motora; no requieren de un proceso de atención riguroso como ocurre utilizando potenciales evocados visuales de estado estable (\textit{steady-state visual evoked potential} - SSVEP) o imaginería motora; no requieren de un estímulo externo como en el caso de SSVEP o P300; y no requieren de tareas cognitivas que generen fatiga muscular o cognitiva como en el caso de la imaginería motora. Adicionalmente, utilizan señales cerebrales que están relacionadas con el área de lenguaje (vocales y sílabas) y tienen la posibilidad de utilizar la innumerable cantidad de palabras (léxico) de un lenguaje. La relevancia de esta tesis está en aportar dos métodologías novedosas de habla silenciosa con EEG, como una opción importante donde las BCIs mejoren su desempeño para controlar dispositivos como: \textit{spellers}, sillas de ruedas, prótesis y robots, entre otros. En esta tesis, una máquina de soporte vectorial para clasificación multiclase es implementada usando la estrategia uno contra el resto (1-\textit{rest}) y uno contra uno (1-1) con una función kernel de base radial. Los parámetros óptimos son calculados con un algoritmo genético. Los resultados son demostrados con la clasificación de cinco vocales (/a/, /e/, /i/, /o/, /u/) y cinco sílabas (/fa/, /pa/, /ma/, /la/ /ra/), usando habla silenciosa con señales electroencefalográficas. El desempeño de las metodologías propuestas medidas en términos de exactitud (\textit{accuracy}) son los siguientes: Con la metodología basada en vector de características con entropía de la información y dimensión de regularización, se seleccionaron dos algoritmos SVM de clasificación multiclase (1-\textit{rest}) y (1-1). Los mejores resultados de clasificación fueron obtenidos con el clasificador (1-1) para vocales y sílabas con habla silenciosa. En el caso de vocales con habla silenciosa se alcanzó una exactitud (media estadística) de 69.83%, y en sílabas con habla silenciosa una exactitud (media estadística) de 66.89%. Para la metodología basada en vector de características con datos funcionales, aplicado a vocales y sílabas con habla silenciosa, se seleccionó el algoritmo SVM de clasificación multiclase (1-1). Para el caso de vocales con habla silenciosa se utilizaron ritmos $\delta$, $\delta\theta$ y \delta\theta\alpha. Los mejores resultados de exactitud fueron obtenidos con los ritmos \delta\theta\alpha con una media estadística de 71.92\%. En el caso de sílabas con habla silenciosa se utilizaron ritmos \delta, con los cuales se alcanzó una exactitud (media estadística) de 67.13%. De los resultados de la clasificación se concluye que la exactitud alcanzada para vocales y sílabas con habla silenciosa, utilizando la metodología basada en vector de características con datos funcionales, es más alta que aquella alcanzada con la metodología basada en vector de características con entropía de la información y dimensión de regularización.Abstract, Brain-computer interfaces have medical relevance in the treatment of individuals suffering from motor paralysis or amputation of upper limb or lower limb; however, the difficulty to extract and accurately process, particularly silent speech brain signals, greatly limit its application. This thesis presents two novel processing systems based on electroencephalography with the ability to classify vowels and syllables with silent speech signals. One methodology is based on the features of the information entropy with dimension regularization, and the other one is based on the features of functional data in the Hilbert space L2, using the position data of the electrodes. Among the advantages of the developed methodologies in comparison to other BCI methods, the following can be established: they do not require training wasteful processes as in the case of motor imagery; they do not require a rigorous attention such as using (steady-state visual evoked potential - SSVEP) or motor imagery; they do not require an external stimulus such as in the case of SSVEP or P300; and they do not require cognitive tasks that generate cognitive or muscle fatigue as in the case of motor imagery. In addition to this, they use brain signals that are related to the language area (vowels and syllables) and they have the possibility to work with the countless number of words (vocabulary) of a language. The relevance of this thesis is to provide two novel methodologies of silent speech with EEG, as an important option where BCIs can improve their performance to control devices such as: spellers, wheelchairs, prostheses and robots, among others. In this thesis, a support vector machine for multiclass classification was implemented using the one against-rest (1-rest) and one against-one (1-1) strategy with a radial basis function kernel. The optimal parameters are calculated with a genetic algorithm. The results are demonstrated with the classification of five vowels (/a/, /e/, /i/, /o/, /u/) and five syllables (/ fa/, /pa/, /ma/, /la/ /ra/) using silent speech with electroencephalographic signals. The performance of the proposed methodologies measured in terms of accuracy is as follows: In regard to the feature vector based information entropy and dimension regularization methodology, two multiclass SVM classification algorithms (1-rest) and (1-1) were selected. The best results were obtained with the classifier (1-1) to vowels and syllables with silent speech. In the case of silent speech vowels an accuracy (statistical average) of 69.83% was reached, and for silent speech syllables an accuracy (statistical average) of 66.89% was reached. For the feature vector based functional data methodology, the multiclass SVM classification algorithm (1-1) was selected to vowels and syllables with silent speech. In the case of silent speech vowels $\delta$, $\delta\theta$ and \delta\theta\alpha rhythms were used. The best results of accuracy were obtained with \delta\theta\alpha rhythms, with a statistical average of 71.92 %. In the case of silent speech syllables rhythms were used, where an accuracy (statistical average) of 67.13% was reached. From the classification results it can concluded that the accuracy to the feature vector based functional data methodology to vowels and syllables with silent speech, is higher than the feature vector based information entropy and dimension regularization methodology.Doctorad

Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication

A new theory of mammalian hearing is presented, which accounts for the auditory image in the midbrain (inferior colliculus) of objects in the acoustical environment of the listener. It is shown that the ear is a temporal imaging system that comprises three transformations of the envelope functions: cochlear group-delay dispersion, cochlear time lensing, and neural group-delay dispersion. These elements are analogous to the optical transformations in vision of diffraction between the object and the eye, spatial lensing by the lens, and second diffraction between the lens and the retina. Unlike the eye, it is established that the human auditory system is naturally defocused, so that coherent stimuli do not react to the defocus, whereas completely incoherent stimuli are impacted by it and may be blurred by design. It is argued that the auditory system can use this differential focusing to enhance or degrade the images of real-world acoustical objects that are partially coherent. The theory is founded on coherence and temporal imaging theories that were adopted from optics. In addition to the imaging transformations, the corresponding inverse-domain modulation transfer functions are derived and interpreted with consideration to the nonuniform neural sampling operation of the auditory nerve. These ideas are used to rigorously initiate the concepts of sharpness and blur in auditory imaging, auditory aberrations, and auditory depth of field. In parallel, ideas from communication theory are used to show that the organ of Corti functions as a multichannel phase-locked loop (PLL) that constitutes the point of entry for auditory phase locking and hence conserves the signal coherence. It provides an anchor for a dual coherent and noncoherent auditory detection in the auditory brain that culminates in auditory accommodation. Implications on hearing impairments are discussed as well.Comment: 603 pages, 131 figures, 13 tables, 1570 reference

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions