System Identification I

Preface IIGeneral Remarks on Japanese Art Culture IIIZen and the Study of Confucianism IVZen and the Samurai VZen and Swordsmanship I VIZen and Swordsmanship II VIIZen and Haiku VIIIZen and the Art of Tea I IXZen and the Art of Tea II XRikyu and Other Teamen XILove of Nature Appendices ITwo Mondo from the \"Hekigan-shu\" IIThe Vimalakirti Sutra III\"Yama-uba,\" a No Play IVThe Swordsman and the Cat VChuang-tzu Bibliography Index

Zen and Japanese Culture

Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.

Review of semi-dry electrodes for EEG recording

As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision. While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored. We propose a region-level stacked bi-directional deep learning framework for EEG-based image classification. Inspired by the hemispheric lateralization of human brains, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The stacked bi-directional long short-term memories are used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequences. Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing state-of-the-arts, our framework achieves outstanding performances in EEG-based classification of brain activities evoked by images. In addition, we find that the signals of Gamma band are not only useful for achieving good performances for EEG-based image classification, but also play a significant role in capturing relationships between the neural activations and the specific emotional states. Our proposed framework provides an improved solution for the problem that, given an image used to stimulate brain activities, we should be able to identify which class the stimuli image comes from by analyzing the EEG signals. The region-level information is extracted to preserve and emphasize the hemispheric lateralization for neural functions or cognitive processes of human brains. Further, stacked bi-directional LSTMs are used to capture the dynamic correlations hidden in EEG data. Extensive experiments on standard EEG-based image classification dataset validate that our framework outperforms the existing state-of-the-arts under various contexts and experimental setups.

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EEG-based image classification via a region-level stacked bi-directional deep learning framework

Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2 s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529 bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.

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Analyzing text recognition from tactually evoked EEG.

Electroencephalography (EEG) is a highly promising medium for brain-computer interfaces (BCI) with potentially extraordinary applications such as the direct control of prosthetics and exoskeletons. However, effective analysis and modeling of EEG data has been limited by its poor spatial resolution. EEG's low amplitude, brief and sporadic nature, compounded by extra-cranial noise, contribute to the difficulty of this problem. This systematic analysis provides strong evidence to guide future research in machine learning applied to real-time analysis of brain states using EEGs. The main goal of this research is to understand how the construction of data sets used in training models affects the accuracy of prominent machine learning algorithms, specifically: Random Forest, Boosting, Naive Bayesian Classifier, k-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Herein, we present a systematic method (N = 153) to test the accuracy of prominent machine learning algorithms when varying three main components of the training data set: the permutations of subjects, the number of unique data sets used to generate the training data set, and the number of samples in each training data set. Our results strongly indicate that Random Forest consistently yields superior results when analyzing EEG data compared to other prominent machine learning algorithms. Furthermore, a pilot investigation was conducted on a mean-normalized feature for EEG data analysis. The pilot analysis (N = 28) confirmed Random Forest's analytical superiority in EEG data, shows signs of improved accuracy, and identifies a distinctive correlation between beta and delta waves and their respective active or idle brain states.

Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.

Using EEG Data Analytics to Measure Meditation

Generalized autocalibrating partially parallel acquisition (GRAPPA) has been a widely used parallel MRI technique. However, noise deteriorates the reconstructed image when reduction factor increases or even at low reduction factor for some noisy datasets. Noise, initially generated from scanner, propagates noise-related errors during fitting and interpolation procedures of GRAPPA to distort the final reconstructed image quality. The basic idea we proposed to improve GRAPPA is to remove noise from a system identification perspective. In this paper, we first analyze the GRAPPA noise problem from a noisy input-output system perspective; then, a new framework based on errors-in-variables (EIV) model is developed for analyzing noise generation mechanism in GRAPPA and designing a concrete method-instrument variables (IV) GRAPPA to remove noise. The proposed EIV framework provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method. Experimental results show that the proposed reconstruction algorithm can better remove the noise compared to the conventional GRAPPA, as validated with both of phantom and in vivo brain data.

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Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the number of subjects that contributes to the training data affects misclassification rate. Furthermore, we discuss how sample entropy contributes to building a classifier. Our results show that classification based on sample entropy give the smallest misclassification rate. Moreover, two data sets were collected from one channel and seven channels respectively. The classification results of each data set show that the more channels we use, the less misclassification we have. Our results show that it is promising to build a self-adaptive classification system by using EEG data to distinguish idle from active state.

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Statistical Machine Learning in Brain State Classification using EEG Data

We present some information depicting the current status of EEG research with projected applications in the areas of health care. We describe a method of quick prototyping an EEG headset, in a cost-effective way and with state-of-the-art technologies. We use meditation research to reach out to the high-end applications of EEG data analysis in understanding human brain states and assisting in promoting human health care. Some devotees to the practices of transcendental meditation have shown the ability to control these brain states. We want to numerically prove or disprove this assumption; the analysis of these states could be the initial step in a process to first predict and later allow individuals to control these states. To this end, we begin to build a system for dynamic and onsite brain state analysis using EEG data. The system will allow users to transit EEG data to an online database through mobile devices, interact with the web server through web interface, and get feedback from EEG data analysis programs on real-time bases.

Hong Lin

Papers

Systematic analysis of machine learning algorithms on EEG data for brain state intelligence

Using EEG Data Analytics to Measure Meditation

Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

Statistical Machine Learning in Brain State Classification using EEG Data

EEG Visualization and Analysis Techniques