Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

Early multisensory attention as a foundation for learning in multicultural Switzerland

Traditional laboratory research on visual attentional control has largely focused on adults, treated one sensory modality at a time, and neglected factors that are a constituent part of information processing in real-world contexts. Links between visual-only attentional control and children’s educational skills have emerged, but they still do not provide enough information about school learning. The present thesis addressed these gaps in knowledge through the following aims: 1) to shed light on the development of the neuro-cognitive mechanisms of attention engaged by multisensory objects in a bottom-up fashion, together with attentional control over visual objects in a top-down fashion, 2) to investigate the links between developing visual and multisensory attentional control and children’s basic literacy and numeracy attainment, and 3) to explore how contextual factors, such as the temporal predictability of a stimulus or the semantic relationships between stimulus features, further influence attentional control mechanisms. To investigate these aims, 115 primary school children and 39 adults from the French-speaking part of Switzerland were tested on their behavioural performance on a child-friendly, multisensory version of the Folk et al. (1992) spatial cueing paradigm, while 129-channel EEG was recorded. EEG data were analysed in a traditional framework (the N2pc ERP component) and a multivariate Electrical Neuroimaging (EN) framework. Taken together, our results demonstrated that children’s visual attentional control reaches adult-like levels at around 7 years of age, or 3rd grade, although children as young as 5 (at school entry) may already be sensitive to the goal- relevance of visual objects. Multisensory attentional control may develop only later. Namely, while 7-year-old children (3rd grade) can be sensitive to the multisensory nature of objects, such sensitivity may only reach an adult-like state at 9 years of age (5th grade). As revealed by EN, both bottom-up multisensory control of attention and top-down visual control of attention are supported by the recruitment of distinct networks of brain generators at each level of schooling experience. Further, at each level of schooling, the involvement of specific sets of brain generators was correlated with literacy and numeracy attainment. In adults, visual and multisensory attentional control were further jointly influenced by contextual factors. The semantic relationship between stimulus features directly influenced visual and multisensory attentional control. In the absence of such semantic links, however, it was the predictability of stimulus onset that influenced visual and multisensory attentional control. Throughout this work, the N2pc component was not sensitive to multisensory or contextual effects in adults, or even traditional visual attention effects in children, and it was owing to EN that the mechanisms of visual and multisensory attentional control were clarified. The present thesis demonstrates the strength of combining behavioural and EEG/ERP markers of attentional control with advanced EEG analytical techniques for investigating the development of attentional control in settings that closely approximate those that we encounter in everyday life

Design of robust ultra-low power platform for in-silicon machine learning

The rapid development of machine learning plays a key role in enabling next generation computing systems with enhanced intelligence. Present day machine learning systems adopt an "intelligence in the cloud" paradigm, resulting in heavy energy cost despite state-of-the-art performance. It is therefore of great interest to design embedded ultra-low power (ULP) platforms with in-silicon machine learning capability. A self-contained ULP platform consists of the energy delivery, sensing and information processing subsystems. This dissertation proposes techniques to design and optimize the ULP platform for in-silicon machine learning by exploring a trade-off that exists between energy-efficiency and robustness. This trade-off arises when the information processing functionality is integrated into the energy delivery, sensing, or emerging stochastic fabrics (e.g., CMOS operating in near-threshold voltage or voltage overscaling, and beyond CMOS devices). This dissertation presents the Compute VRM (C-VRM) to embed the information processing into the energy delivery subsystem. The C-VRM employs multiple voltage domain stacking and core swapping to achieve high total system energy efficiency in near/sub-threshold region. A prototype IC of the C-VRM is implemented in a 1.2 V, 130 nm CMOS process. Measured results indicate that the C-VRM has up to 44.8% savings in system-level energy per operation compared to the conventional system, and an efficiency ranging from 79% to 83% over an output voltage range of 0.52 V to 0.6 V. This dissertation further proposes the Compute Sensor approach to embed information processing into the sensing subsystem. The Compute Sensor eliminates both the traditional sensor-processor interface, and the high-SNR/high-energy digital processing by moving feature extraction and classification functions into the analog domain. Simulation results in 65 nm CMOS show that the proposed Compute Sensor can achieve a detection accuracy greater than 94.7% using the Caltech101 dataset, which is within 0.5% of that achieved by an ideal digital implementation. The performance is achieved with 7x to 17x lower energy than the conventional architecture for the same level of accuracy. To further explore the energy-efficiency vs. robustness trade-off, this dissertation explores the use of highly energy efficient but unreliable stochastic fabrics to implement in-silicon machine learning kernels. In order to perform reliable computation on the stochastic fabrics, this dissertation proposes to employ statistical error compensation (SEC) as an effective error compensation technique. This dissertation makes a contribution to the portfolio of SEC by proposing embedded algorithmic noise tolerance (E-ANT) for low overhead error compensation. E-ANT operates by reusing part of the main block as estimator and thus embedding the estimator into the main block. System level simulation results in a commercial 45 nm CMOS process show that E-ANT achieves up to 38% error tolerance and up to 51% energy savings compared with an uncompensated system. This dissertation makes a contribution to the theoretical understanding of stochastic fabrics by proposing a class of probabilistic error models that can accurately model the hardware errors on the stochastic fabrics. The models are validated in a commercial 45 nm CMOS process and employed to evaluate the performance of machine learning kernels in the presence of hardware errors. Performance prediction of a support vector machine (SVM) based classifier using these models indicates that the probability of detection P_{det} estimated using the proposed model is within 3% for timing errors due to voltage overscaling when the error rate p_η ≤ 80%, within 5% for timing errors due to process variation in near threshold-voltage (NTV) region (0.3 V-0.7 V) and within 2% for defect errors when the defect rate p_{saf} is between 10^{-3} and 20%, compared with HDL simulation results. Employing the proposed error model and evaluation methodology, this dissertation explores the use of distributed machine learning architectures, named classifier ensemble, to enhance the robustness of in-silicon machine learning kernels. Comparative study of distributed architectures (i.e., random forest (RF)) and centralized architectures (i.e., SVM) is performed in a commercial 45 nm CMOS process. Employing the UCI machine learning repository as input, it is determined that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors in the NTV region (0.3 V- 0.7 V). Additionally, an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric is proposed to further enhance the robustness of RF architectures. Simulation results confirm that the error weighted voting technique achieves a P_{det} that varies by only 1.4%, which is 12x lower compared to centralized architectures

Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings

This doctoral thesis outlines advances in multi-way analysis for characterizing electroencephalogram (EEG) recordings from a paediatric population, with the aim to describe new links between EEG data and changes in the brain. This entails establishing the validity of multi-way analysis as a framework for identifying developmental information at the individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear algebraic architecture to identify latent structural relationships in naturally occurring higher order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a multi-way model to efficiently express the complex structures present in paediatric EEG recordings as unique combinations of low-rank matrices, offering new insights into child development. This multi-way CPD framework is explored for both typically developing (TD) children and children with potential developmental delays (DD), e.g. children who suffer from epilepsy or paediatric stroke. Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric EEG via multi-way analysis. Here, the CPD model probes the underlying relationships between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the CPD can reveal distinct population-level features in rEEG that reflect unique developmental traits in varying child populations. These development-affiliated profiles are evaluated with respect to capturing structures well-established in childhood EEG. The identified features are also interrogated for their predictive abilities in anticipating new subjects’ ages. Assessing simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis framework as well suited for identifying developmental profiles from paediatric rEEG. We extend the multi-way analysis scheme to more complex EEG scenarios common in EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of multi-way modelling for interventions where developmental changes often pose as barriers. The multi-way CPD model is expanded to include four modes- task, spatial, spectral and subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual attention task that elucidates a steady-state visual evoked potential and present the advantages gained from the extended CPD model. Through direct multi-linear projection, we demonstrate that linear profiles of the CPD can be capitalized upon for rapid task classification sans individual subject classifier calibration. Incorporating concepts from the multi-way analysis scheme with child development measured by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for inferring development from paediatric EEG. We utilize a common EEG task (button-press) to establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model and cognitive scores from psychometric evaluations then permits joint decomposition of the two datasets to identify common features associated with each representation of development. Use of grid search optimization and a fully cross-validated design supports the JEDI model as another technique for rapidly discerning the developmental status of a child via EEG. We then briefly turn our attention to associating child development as measured by psychometric tests to markers in the EEG using graph network properties. Using graph networks, we show how the functional connectivity can inform on potential developmental delays in very young epileptic children using routine, clinical rEEG measures. This establishes a potential tool complementary to the JEDI model for identifying and inferring links between the established psychometric evaluation of developing children and functional analysis of the EEG. Multi-way analysis of paediatric EEG data offers a new approach for handling the developmental status and profiles of children. The CPD model offers flexibility in terms of identifying development-related features, and can be integrated into EEG tasks common in rehabilitation paradigms. We aim for the multi-way framework and associated techniques pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for characterizing paediatric development

Oscillatory mechanisms of conscious perception and attention

Although the prominent role of neural oscillations in perception and cognition has been continuously investigated, some critical questions remain unanswered. My PhD thesis was aimed at addressing some of them. First, can we dissociate oscillatory underpinnings of perceptual accuracy and subjective awareness? Current work would strongly suggest that this dissociation can be drawn. While the fluctuations in alpha-amplitude decide perceptual bias and metacognitive abilities, the speed of alpha activity (i.e., alpha-frequency) dictates sensory sampling, shaping perceptual accuracy. Second, how are these oscillatory mechanisms integrated during attention? The obtained results indicate that a top-down visuospatial mechanism modulates neural assemblies in visual areas via oscillatory re-alignment and coherence in the alpha/beta range within the fronto-parietal brain network. These perceptual predictions are reflected in the retinotopically distributed posterior alpha-amplitude, while perceptual accuracy is explained by the higher alpha-frequency at the to-be-attended location. Finally, sensory input, elaborated via fast gamma oscillations, is linked to specific phases of this slower activity via oscillatory nesting, enabling integration of the feedback-modulated oscillatory activity with sensory information. Third, how can we relate this oscillatory activity to other neural markers of behaviour (i.e., event-related potentials)? The obtained results favour the oscillatory model of ERP genesis, where alpha-frequency shapes the latency of early evoked-potentials, namely P1, with both neural indices being related to perceptual accuracy. On the other hand, alpha-amplitude dictates the amplitude of later P3 evoked-response, whereas both indices shape subjective awareness. Crucially, by combining different methodological approaches, including neurostimulation (TMS) and neuroimaging (EEG), current work identified these oscillatory-behavior links as causal and not just as co-occurring events. Current work aimed at ameliorating the use of the TMS-EEG approach by explaining inter-individual differences in the stimulation outcomes, which could be proven crucial in the way we design entrainment experiments and interpret the results in both research and clinical settings

From working memory to maths: A multi-measure physiological investigation of the impact of anxiety

The present research project adopted physiological measures to investigate the rela- tionship between anxiety and working memory (WM). The importance of WM for maths processing and the sensitivity of physiological measures to both anxious responses and executive processes make these measures appealing for advancing our understanding of maths anxiety (MA). The overarching research question asked whether anxiety affects the accuracy of the representations in WM, process that is thought to be central in performing maths task and that may explain the relationship between maths performance and MA. In this thesis the construct of anxiety was considered under two aspects. First, as source of noise causing a decrease in performance to tasks relying on WM. Second, as evolutionary determined physiological reaction to stressors. The former conceptualization of anxiety informed the parts of this thesis investigating the effects of anxiety over WM. The latter provided the starting point to investigate how physiological measures may provide insights on the relationship between the effects of MA on WM processes and maths performance. The first two studies in the thesis aimed at addressing the question of whether anxiety affects WM. WM was conceptualized as a limited capacity system where information is temporarily maintained and manipulated in order to perform a task. It is necessary for performing complex cognitive tasks as well as for later encoding of information in long term memory (Baddeley, 1992). The efficiency of such system can be compromised by events external or internal to the individual. One factor that may impact WM efficiency is anxiety. Anxiety is a state of tension in the face of threat and it is characterised by worry and increased physiological arousal. Task-irrelevant self-directed thoughts might reduce WM resources otherwise needed for performing cognitive tasks (Eysenck et al., 2007). Moreover, the effect that anxiety has on maths performance, as in the case of maths anxiety (MA), might be explained by the effect of anxiety on WM processing (Ashcraft and Kirk, 2001). The present thesis investigates the interplay between these three constructs with the aid of physiological measures. Attentional control has been found to be a central function of WM processing (Baddeley, 1992; Eysenck, 2007). In the first experiment I look at the involvement of attentional processes in memory encoding by investigating whether ERP prestimulus-memory effects (pSMEs) are of attentive nature (Otten et al., 2006). If that was the case, investigating how anxiety affected attentional processed prior memory encoding would have provided a starting point to address how MA may exert its influence on the performance of maths tasks. Contrary to my prediction, the pSME seemed to be better explained by semantic processing, rather than recruitment of attentional processes. In the second experiment, I investigated how anxiety affects maintenance in WM. To this aim I used experimentally induced by threat-of-shock might impact WM as conceptualized by resource models. Resource models of WM give a central role to attentional resources (Ma et al, 2014). To do so I implemented a modified NPU-threat test protocol (Shmitz & Grillon, 2012) combined with task in which WM was assessed through precision of recall (Bays et al., 2009) and anxiety was assessed by means of the startle reflex. In the different conditions, the probability of receiving a shock was manipulated and the estimates of the distributions fitted to the data were compared to assess whether anxiety decreased precision of recall. In a third experiment I looked at the specific case of MA. It is thought that MA impairs maths performance by depleting WM resources (Ashcraft and Kirk, 2001). While MA impacts on implicit processes such as WM, very few studies have attempted to investigate implicit measures of MA with varying success. In the experiment I attempt to replicate the behavioural findings of Rubinsten et al. (2010 and 2012) in which they use an affective priming task combined with a verification task. Furthermore, I investigate the startle reflex and heart rate variability as implicit physiological measures, which have never been studied in the context of MA.Gates Cambridg

ON THE REPRESENTATION OF SPATIAL AND TEMPORAL STRUCTURES: EFFECTS IN HUMAN VISUOSPATIAL WORKING MEMORY

In a diverse range of environments, each replete with unique physical phenomena, humans are capable of acting and achieving with volition. To do so we capitalize upon structures that exist in the physical world, rapidly drawing associations and forming conceptual relationships between items and occurrences. In this dissertation work, I examine how structures in the domains of space and time impact the representations of information that we form and hold in working memory, in the service of goal-driven behavior. Three key findings arise from the studies I present herein. First, representation of spatial structures in working memory is supported by oscillatory neural activity that differs between individuals based upon biological sex. The peak of posterior alpha frequency oscillatory activity is modulated in support of visuospatial representation maintenance more so in females than males. Among males but not females, successful representation of relative spatial structure is positively tied to an individual’s peak frequency of alpha oscillatory activity. Second, the interaction of spatial and temporal structures across perceptual modalities impacts representation in working memory. Shared temporal structure between a stream of visual targets and a stream of sounds promotes representation of the spatial structure of those sounds. This integration of perceptual information occurs whether helpful or harmful, differentially impacting performance. Third, the representation of spatial information in working memory is impacted by a particular form of temporal structure — rhythm. The presence of rhythmic versus arrhythmic temporal structure within a visuospatial stream does not increase the precision of working memory representation, but rather increases the speed with which representations may be formed. Rhythmic structure spontaneously and consistently facilitates working memory performance. Arrhythmic structure may hinder temporal processing but can be behaviorally compensated for with the application of controlled attention to the temporal domain. A novel paradigm, designed and utilized to study effects of rhythmic temporal structure upon visuospatial working memory is described

Motivational underpinnings of negative affect as revealed by emotional modulation of EEG bands

The studies reported in this thesis pertain to a project aiming at investigating the motivational underpinnings of psychopathologies characterized by negative affect, such as anxiety and depression. Unbalanced or conflicting motivational tendencies can lead to disturbances in emotional responding and ongoing affect, which are often associated with psychopathology. As it will be introduced in the first chapter, motivational drives are thought to be sustained by two main systems in the brain, namely the appetitive and the defensive motivational circuits. The appetitive system contributes to approach behaviors in response to rewarding and pleasant stimuli. On the other side, the defensive system drives withdrawal from threat and is important for triggering unpleasant emotions. Therefore, emotions can be described as action dispositions reflecting basic tendencies to both approach and withdrawal in response to emotional stimuli. Several models postulated that negative affect in psychopathology arises from an excessive activation of the defensive system, which leads to an increased and dominant tendency to actively withdrawal from potential threats in the environment. Partially contrasting with this theoretical conceptualization, the aim of this thesis was to investigate whether negative affect could also manifest in psychopathologies which are not characterized by a straightforward increase in withdrawal tendencies. In this sense, EEG correlates of motivational tendencies in response to emotional stimuli were investigated in blood phobia and in dysphoria (i.e., subclinical depression). Accordingly, blood phobia, contrary to other specific phobias, is not associated with an increase in action disposition in response to the feared stimulus. Therefore, negative affect in these individuals does not seem to arise from a pronounced tendency to actively withdrawal. Depression represents another example of condition in which it is not clear whether negative affect is subtended by a dominance of the withdrawal system or by a lack in appetitive motivation. Accordingly, it is matter of debate whether depressed mood is due to preferential processing of unpleasant stimuli or reduced sensitivity to rewards and positive emotions. In order to investigate these aspects, three studies were conducted. In study 1, it was chosen to investigate modulation of EEG bands during an emotional Go/Nogo task in blood phobia. The emotional Go/Nogo task, including phobia-related pictures, along with phobia-unrelated unpleasant, neutral and pleasant stimuli, was ideal to investigate the lack of action disposition in blood phobia. Results showed that individuals with blood phobia display a conflicting motivational pattern, characterized by co-occurring tendencies to attend and avoid the feared stimulus, in strong contrast with other phobias. In Studies 2 and 3, modulation of EEG bands during an emotional imagery task in individuals with dysphoria was investigated. The emotional imagery is an active task, in which individuals are requested to actively imagine emotional scenarios; therefore it was well suited to investigate emotional modulation of appetitive and defensive motivational tendencies. Overall, results supported the idea that depressed mood in dysphoria is due to a lack in appetitive motivation, accompanied by a reduction in processing of pleasant stimuli. Again, we found no evidences of increased defensive motivation and tendency to withdrawal in dysphoric individuals. Finally, our research focused on possible clinical implications of the abovementioned findings, concerning the application of bio-behavioral trainings for the reduction of negative affect. In line with the pertaining literature, results from of our first three studies showed that frontal alpha asymmetry is an informative index of motivational tendencies underlying affect and emotional responses. Therefore, a fourth study was conducted, aimed at evaluating the effectiveness of a frontal alpha asymmetry neurofeedback training in reducing negative affect, anxiety and depressive symptoms in healthy individuals. After five training sessions, healthy individuals succeeded in reducing right compared to left prefrontal activity, through a specific increase in right frontal alpha. In accordance with the role of the right prefrontal cortex in defensive motivation and negative affect, this increase in right frontal alpha power was associated with a significant reduction in negative affect and anxiety. In conclusion, the present thesis confirms and extends the link between motivational tendencies and negative affect, showing that negative affect is not exclusively associated with an increase in defensive motivation and in active withdrawal disposition. Accordingly, among psychopathologies characterized by negative affect, blood phobia and dysphoria do not display the typically predicted motivational pattern. A first step toward the transition from basic research to clinical application has been proposed, with the implementation of an EEG-based neurofeedback for the reduction of negative affect. Overall, the present work is of potential relevance for a better understanding of the motivational underpinnings of psychopathologies characterized by negative affect, also providing a strong rationale for the application of bio-behavioral trainings