Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

Detecting event-related recurrences by symbolic analysis: Applications to human language processing

Quasistationarity is ubiquitous in complex dynamical systems. In brain dynamics there is ample evidence that event-related potentials reflect such quasistationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study we elaborate a recent approach for detecting quasistationary states as recurrence domains by means of recurrence analysis and subsequent symbolisation methods. As a result, recurrence domains are obtained as partition cells that can be further aligned and unified for different realisations. We address two pertinent problems of contemporary recurrence analysis and present possible solutions for them.Comment: 24 pages, 6 figures. Draft version to appear in Proc Royal Soc

Adaptive Multi-Output Gradient RBF Tracker For Nonlinear and Nonstationary Regression

Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.</p

A Deep Learning Approach to Analyzing Continuous-Time Systems

Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to the performance of models of complex processes, but deep learning is generally not used for scientific analysis. Here we show that deep learning can be used to analyze complex processes, providing flexible function approximation while preserving interpretability. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many natural systems and may critically affect the interpretation of data. We evaluate our model on incremental human language processing, a domain with complex continuous dynamics. We demonstrate substantial improvements on behavioral and neuroimaging data, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions that are otherwise hard to study.Comment: Main article: 12 pages, 1 table, 3 figures; Supplementary Information: 54 pages, 6 tables, 30 figure

Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space. Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. Meaningful training examples are acquired as the agent explores different regions of its state/action space. When the agent is in one such region, only highly correlated samples from that region are typically acquired. Moreover, the regions that the agent is likely to visit will depend on its current policy, suggesting that an agent that has a good policy may avoid exploring particular regions. The confluence of these factors means that without some mitigation techniques, supervised neural networks as function approximation in temporal-difference learning will be restricted to the simplest test cases. This work explores catastrophic forgetting in neural networks in terms of supervised and reinforcement learning. A simple mathematical model is introduced to argue that catastrophic forgetting is a result of overlapping representations in the hidden layers in which updates to the weights can affect multiple unrelated regions of the input space. A novel neural network architecture, dubbed cluster-select, is introduced which utilizes online clustering for the selection of a subset of hidden neurons to be activated in the feedforward and backpropagation stages. Clusterselect is demonstrated to outperform leading techniques in both classification nd regression. In the context of reinforcement learning, cluster-select is studied for both fully and partially observable Markov decision processes and is demonstrated to converge faster and behave in a more stable manner when compared to other state-of-the-art algorithms

Linear MMSE-Optimal Turbo Equalization Using Context Trees

Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.Comment: Submitted to the IEEE Transactions on Signal Processin

Model combination in neural-based forecasting

This paper discusses different ways of combining neural predictive models or neural-based forecasts. The proposed approaches consider Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. The usual framework for linearly combining estimates from different models is extended, to cope with the case where the forecasting errors from those models are correlated. A prefiltering methodology is pro posed, addressing the problems raised by heavily nonstationary time series. Moreover, the paper discusses two approaches for decision-making from forecasting models: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models.info:eu-repo/semantics/publishedVersio

데이터 기반 센서 보정 및 모바일 센서 배치를 통한 도시 미세먼지 센서네트워크 정확도 향상

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이동준.Particulate matter (PM) sensor has been widely deployed to increase spatiotemporal resolution in the urban environment. As a cost-effective PM monitoring solution, low-cost PM sensor ideally stands for dense sensor network nodes. However, low-cost PM sensor remains the doubt of its data reliability. In this paper, we investigate the accuracy of low-cost PM sensor by co-locating a governmental beta attenuation monitor (BAM) for 7.5 months and increase the accuracy with data-driven calibration. We research linear/nonlinear calibration (i.e. multiple linear regression (MLR)/multilayer perceptron (MLP)) and introduce a novel combined calibration. The methods are evaluated by field experiments and are compared with other methods and studies. Also, the data-driven calibration model can utilize for but only a co-located sensor node but also other sensor nodes by using a sensor network. The feasibility of sensor network calibration has been evaluated with experiments.도시 대기 질 측정의 시공간 해상도를 증가시키기 위해 미세먼지 센서가 광범위하게 배치되고 있다. 고해상도의 미세먼지 측정을 위한 현실적인 대안으로 저가형 미세먼지가 대표적으로 이용되고 있다. 하지만 저가형 미세먼지 센서의 측정 데이터 신뢰성에 대한 의문점은 해결되지 않고 있다. 본 연구는 저가형 미세먼지 센서의 장기간 정확도 평가를 수행하였으며, 이를 위하여 멀티 센서 플랫폼을 제작하고 이를 고신뢰도의 정부 관측소에 함께 배치하였다. 선형/비선형 추정 모델인 다중 선형회귀 모델과 인공신경망인 다층 퍼셉트론을 적용하여 데이터 기반 모델을 생성하였으며, 이를 통합한 추정 모델을 개발하였다. 이 방법들은 실외 배치 실험을 통해 평가되었으며 타 추정 모델과 타 연구와의 비교 분석을 수행하였다. 또한 관측소에 배치하여 생성된 데이터 기반 모델은 센서 네트워크를 통해 다른 노드에 전달하여 활용할 수 있으며, 이러한 접근에 대한 타당성 평가는 실험을 통해 확인하였다.1 Introduction 1 2 System Description 3 2.1 System Elements 3 2.1.1 Beta Attenuation Monitor 3 2.1.2 Multi-Sensor Platform 4 2.2 System Con guration 8 2.2.1 Sensor Platform Deployment 8 2.2.2 Calibration Procedures and Evaluation 9 3 Data-Driven Sensor Calibration 12 3.1 Related Studies 12 3.1.1 w/o Calibration Model 12 3.1.2 Previous Researches 13 3.2 Linear/Nonlinear Calibration 15 3.2.1 Linear Calibration: Multiple Linear Regression 15 3.2.2 Nonlinear Calibration: Multilayer Perceptron 17 3.2.3 Limitation on Linear/Nonlinear Calibration 19 3.3 SMART calibration 21 3.3.1 Concepts of Calibration 21 3.3.2 Procedures of SMART Calibration 24 3.4 Experiments and Results 26 3.4.1 Comparison w/ Other Calibration Methods 28 3.4.2 Comparison w/ Other Studies 30 3.4.3 Further Analysis of Calibration Model 31 4 Sensor Network Calibration 33 4.1 Related study 34 4.1.1 Sensor Network Calibration 34 4.1.2 Mobile Sensor Node 35 4.2 Transfer Calibration 35 4.2.1 Concepts of Transfer Calibration 35 4.3 Rendezvous Calibration 36 4.4 Experiments and Results 37 5 Conclusion and Future Work 40 5.1 Conclusion 40 5.2 Future Work 41Maste