A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

A low power integrated CMOS transmitter for BCI system

The prototype wafer of a low power integrated CMOS Transmitter for short-range biotelemetry application has been designed and fabricated, which is prospective to be implanted in the human brain to transfer the extracted neural information to the external computer. The transmitter consists of five parts, a bandgap current regulator, a ring oscillator, a buffer, a modulator and a power transistor. High integration and low power are the most distinct criteria for such an implantable integrated circuit. The post-simulation results show that under a 3.3 V power supply the transmitter provides 100.1 MHz half-wave sinusoid current signal to drive the off-chip antenna, the output peak current range is -0.155 mA similar to 1.250 mA, and on-chip static power dissipation is low to 0.374 mW. All the performances of the transmitter satisfy the demands of wireless real-time BCI system for neural signals recording and processing.

基于支持向量多分类机的多类复杂手操作EEG信号模式识别

针对用于服务机器人的脑机接口系统中脑电信号模式识别精度不高,不能满足机器人多任务要求的问题,提出一种基于C-支持向量多分类机的多类复杂手操作EEG信号模式识别方法,并将其应用到复杂手操作的EEG信号模式识别试验中,实现一个4类复杂手操作的模式识别,实验结果表明,与之前用BP神经网络进行识别相比,识别率由85%提高到了90%。

Reti complesse e analisi del segnale elettroencefalografico

The identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analyzed using a phase synchronization based measure, minimum spanning tree and k-core decomposition. The analysis was performed for each classical brain rhythm separately. Furthermore, we aim to provide a network approach insensitive to the effects that epoch length has on functional connectivity (FC) and network reconstruction. Two different measures, the phase lag index (PLI) and the Amplitude Envelope Correlation (AEC), were applied to EEG resting-state recordings for a group of eighteen healthy volunteers. Weighted clustering coefficient (CCw), weighted characteristic path length (Lw) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Results about distinctive functional core, show highest classification rates from k-core decomposition in gamma (EER=0.130, AUC=0.943) and high beta (EER=0.172, AUC=0.905) frequency bands. Results from scalp analysis concerning the influence of epoch length, show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 seconds for PLI and 6 seconds for AEC. Moreover, CCw and Lw show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 seconds versus 4-8 seconds for AEC). At the source-level the results were even more reliable, with stability already at 1 second duration for PLI-based MSTs. Our results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. Regarding epoch length, the present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain network topology between different studies.

Animated virtual agents to cue user attention: comparison of static and dynamic deictic cues on gaze and touch responses

This paper describes an experiment developed to study the performance of virtual agent animated cues within digital interfaces. Increasingly, agents are used in virtual environments as part of the branding process and to guide user interaction. However, the level of agent detail required to establish and enhance efficient allocation of attention remains unclear. Although complex agent motion is now possible, it is costly to implement and so should only be routinely implemented if a clear benefit can be shown. Pevious methods of assessing the effect of gaze-cueing as a solution to scene complexity have relied principally on two-dimensional static scenes and manual peripheral inputs. Two experiments were run to address the question of agent cues on human-computer interfaces. Both experiments measured the efficiency of agent cues analyzing participant responses either by gaze or by touch respectively. In the first experiment, an eye-movement recorder was used to directly assess the immediate overt allocation of attention by capturing the participant’s eyefixations following presentation of a cueing stimulus. We found that a fully animated agent could speed up user interaction with the interface. When user attention was directed using a fully animated agent cue, users responded 35% faster when compared with stepped 2-image agent cues, and 42% faster when compared with a static 1-image cue. The second experiment recorded participant responses on a touch screen using same agent cues. Analysis of touch inputs confirmed the results of gaze-experiment, where fully animated agent made shortest time response with a slight decrease on the time difference comparisons. Responses to fully animated agent were 17% and 20% faster when compared with 2-image and 1-image cue severally. These results inform techniques aimed at engaging users’ attention in complex scenes such as computer games and digital transactions within public or social interaction contexts by demonstrating the benefits of dynamic gaze and head cueing directly on the users’ eye movements and touch responses.

Exploring processes of indeterminate determinism in music composition, programming and improvisation

This portfolio consists of 15 original musical works. Taking the form of electronic and acousmatic music, multimedia, and scores, these chamber works serve as a result of experimentation and improvisation with individually built computer interfaces. The accompanying commentary provides discourse on the conceptual practice of these interfaces becoming a compositional entity that present a multi-interpretative opportunity to explore, engage, and personalise. Following this, the commentary examines the path of creative decisions and musical choices that formed both these interfaces and the resulting musical and visual works. This portfolio is accompanied by interfaces used, transcoded interfacing behavioural information, and documented improvisational findings.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

Smart object for 3D interaction

This paper reports on the creation of an interface for 3D virtual environments, computer-aided design applications or computer games. Standard computer interfaces are bound to 2D surfaces, e.g., computer mouses, keyboards, touch pads or touch screens. The Smart Object is intended to provide the user with a 3D interface by using sensors that register movement (inertial measurement unit), touch (touch screen) and voice (microphone). The design and development process as well as the tests and results are presented in this paper. The Smart Object was developed by a team of four third-year engineering students from diverse scientific backgrounds and nationalities during one semester.

Modèle novateur de conception d’interface humain-ordinateur centrée sur l’utilisateur : le designer en tant que médiateur

Cette recherche porte sur des questions relatives à la conception des interfaces humain-ordinateur. Elle s’inscrit dans le courant des recherches sur l’utilisabilité et elle s’intéresse particulièrement aux approches centrées sur l’utilisateur. Nous avons été très souvent témoin des difficultés éprouvées par les utilisateurs dans l’usage de certaines interfaces interactives et nous considérons que ces difficultés découlent d’un problème de design. Le design d’interface doit être basé sur les besoins de l’utilisateur dans le cadre de ses activités, dont les caractéristiques devaient être bien comprises et bien prises en considération pour mener à la conception d’interfaces qui respectent les critères d’utilisabilité. De plus, la communauté des chercheurs ainsi que l’industrie admettent maintenant que pour améliorer le design, il est crucial de développer les interfaces humain-ordinateur au sein d’une équipe multidisciplinaire. Malgré les avancées significatives dans le domaine du design centrées sur l’utilisateur, les visées annoncées sont rarement réalisées. La problématique étudiée nous a conduit à poser la question suivante : En tant que designer d’une équipe multidisciplinaire de conception, comment modifier la dynamique de collaboration et créer les conditions d’une conception véritablement centrée sur l’interaction humain-ordinateur ? Notre démarche de recherche a été guidée par l’hypothèse voulant que l’activité de design puisse être le moyen de faciliter la création d’un langage commun, des échanges constructifs entre les disciplines, et une réflexion commune centrée sur l’utilisateur. La formulation de cette hypothèse nous a mené à réfléchir sur le rôle du designer. Pour mener cette recherche, nous avons adopté une méthodologie mixte. Dans un premier temps, nous avons utilisé une approche de recherche par projet (recherche-projet) et notre fonction était celle de designer-chercheur. La recherche-projet est particulièrement appropriée pour les recherches en design. Elle privilégie les méthodes qualitatives et interprétatives ; elle étudie la situation dans sa complexité et de façon engagée. Nous avons effectué trois études de cas successives. L’objectif de la première étude était d’observer notre propre rôle et nos interactions avec les autres membres de l’équipe de projet pendant le processus de design. Dans la seconde étude, notre attention a été portée sur les interactions et la collaboration de l’équipe. Nous avons utilisé le processus de design comme méthode pour la construction d’un langage commun entre les intervenants, pour enrichir les réflexions et pour favoriser leur collaboration menant à redéfinir les objectifs du projet. Les limites de ces deux cas nous ont conduit à une intervention différente que nous avons mise en œuvre dans la troisième étude de cas. Cette intervention est constituée par la mise en place d’un atelier intensif de conception où les intervenants au projet se sont engagés à développer une attitude interdisciplinaire permettant la copratique réflexive pour atteindre les objectifs d’un projet de construction d’un site web complexe centré sur l’utilisateur. L’analyse et l’interprétation des données collectées de ces trois études de cas nous ont conduit à créer un modèle théorique de conception d’interface humain-ordinateur. Ce modèle qui informe et structure le processus de design impliquant une équipe multidisciplinaire a pour objectif d’améliorer l’approche centrée sur l’utilisateur. Dans le cadre de ce modèle, le designer endosse le rôle de médiateur en assurant l’efficacité de la collaboration de l’équipe. Dans un deuxième temps, afin de valider le modèle et éventuellement le perfectionner, nous avons utilisé une approche ethnographique comportant des entrevues avec trois experts dans le domaine. Les données des entrevues confirment la validité du modèle ainsi que son potentiel de transférabilité à d’autres contextes. L’application de ce modèle de conception permet d’obtenir des résultats plus performants, plus durables, et dans un délai plus court.

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

Example Based Image Analysis and Synthesis

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.

Low cost eye tracking for human-machine interfacing

One of the main challenges for developers of new human-computer interfaces is to provide a more natural way of interacting with computer systems, avoiding excessive use of hand and finger movements. In this way, also a valuable alternative communication pathway is provided to people suffering from motor disabilities. This paper describes the construction of a low cost eye tracker using a fixed head setup. Therefore a webcam, laptop and an infrared lighting source were used together with a simple frame to fix the head of the user. Furthermore, detailed information on the various image processing techniques used for filtering the centre of the pupil and different methods to calculate the point of gaze are discussed. An overall accuracy of 1.5 degrees was obtained while keeping the hardware cost of the device below 100 euros.

A question of identity - wiring in the human

This paper discusses the RFID implants for identification via a sensor network. Brain-computer implants linked in to a wireless network. Biometric identification via body sensors is also discussed. The use of a network as a means for remote and distance monitoring of humans opens up a range of potential uses. Where implanted identification is concerned this immediately offers high security access to specific areas by means of only an RFID device. If a neural implant is employed then clearly the information exchanged with a network can take on a much richer form, allowing for identification and response to an individual's needs based on the signals apparent on their nervous system.