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Transfer Learning for EEG-Based Brain-computer Interfaces: A Review of Progress Made Since 2016

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

Work-in-Progress: Multiple Alignment of Packet Sequences for Efficient Communication in a Many-Core Neuromorphic System

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Work-in-Progress: Multiple Alignment of Packet Sequences for...

Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： Gianvito Urgese Luca Peres Francesco Barchi Enrico Macii Andrea Acquaviva Department of Control and Computer Engineering Politecnico di Torino Italy

In this era, the requirement of high-performance computing at low power cost can be met by the parallel execution of an application on a large number of programmable cores. Emerging many-core architectures provide dense interconnection fabrics leading to new communication requirements. In particular, the effective exploitation of synchronous and asynchronous channels for fast communication from/to internal cores and external devices is a key issue for these architectures. In this paper, we propose a methodology for clustering sequential commands used for configuring the parallel execution of tasks on a globally asynchronous locally synchronous multi-chip many-core neuromorphic platform. With the purpose of reducing communication costs and maximise the exploitation of the available communication bandwidth, we adapted the Multiple Sequence Alignment (MSA) algorithm for clustering the unicast streams of packets used for the configuration of each core so as to generate a coherent multicast stream that configures all cores at once. In preliminary experiments, we demonstrate how the proposed method can lead up to a 97% reduction in packet transmission thus positively affecting the overall communication cost.

关键词： Unicast computer architecture Neuromorphics Clustering algorithms Task analysis Routing Computational modeling

Work-in-Progress: Impact of Graph Partitioning on SNN Placement for a Multi-Core Neuromorphic Architecture

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Work-in-Progress: Impact of Graph Partitioning on SNN Placem...

Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： Francesco Barchi Gianvito Urgese Enrico Macii Andrea Acquaviva Department of Control and Computer Engineering Politecnico di Torino Italy

In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. We designed a task placement pipeline capable of analysing the network of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuronto-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected network components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating network-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

关键词： Neurons Sociology Statistics computer architecture Simulated annealing Program processors Brain modeling

Modal regression based structured low-rank matrix recovery for multi-view learning

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study ⁎

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IFAC-PapersOnLine 2019年第1期52卷 772-777页

作者： Anna Stief James R. Ottewill Jerzy Baranowski ABB Corporate Research Center ul. Starowislna 13a 31-038 Krakow Poland Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 30-059 Krakow Poland

Feature design and selection is one of the first steps towards successful fault detection and diagnosis. Data from different sources can contain complimentary information about a monitored system. Hence methods which fuse features from multiple sources can often detect and diagnose a greater number of fault modes with higher confidence. However, solutions that require data from multiple sensors as inputs can be susceptible to failure if one or more of those sensors cease to function. Optimally a solution will fuse data from a sufficient number of sensors so that the advantages of sensor fusion are realized, while the robustness of the system is retained. In this paper the authors investigate how the best subset of features might differ for fault detection and fault severity diagnosis in a multiphase flow facility case study. ReliefF, which is a K-nearest neighbors-based feature selection filter, is used to rank the features for different problems. The dataset used for the analysis contains data from various operating conditions and induced faults with various severities. It was found that the optimal subset of features varied for different monitoring problems. It was also shown that including features that are ranked as being uninformative into a fault classifier can also impact the robustness of the classifier to sensor failures.

关键词： Signal processing Feature extraction Feature selection Process monitoring

A comprehensive study and comparison of core technologies for MPEG 3D point cloud compression

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arXiv 2019年

作者： Liu, Hao Yuan, Hui Liu, Qi Hou, Junhui Liu, Ju School of Information Science and Engineering Shandong University Qingdao266200 China School of Control Science and Engineering Shandong University Jinan250000 China Department of Computer Science City University of Hong Kong Hong Kong999077 Hong Kong

Point cloud based 3D visual re resentation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Color

Autonomous Swimmers Tracking Algorithm Based on Kalman Filter and CamShift

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Autonomous Swimmers Tracking Algorithm Based on Kalman Filte...

International Conference on Open Source Systems and Technologies (ICOSST)

作者： Muhammad Aftab Hayat Goutian Yang Atif Iqbal Adeel Saleem Muhammad Mateen School of Control & Computer Engineering North China Electric power University Beijing China School of Renewable Energy & Clean Energy North China Electric power University Beijing China Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (数字)9781728146133

ISBN: (纸本)9781728146140

The visual system is the most important sensory organ of human beings. Most of the external information obtained by human beings comes from vision. With the continuous exploration of the natural world by human beings, people always hope to realize the function of human perception through some digital machine and automatically acquire the information of the outside world. The traditional CamShift algorithm needs to manually locate the target during tracking, and it can be used in complex backgrounds such as color interference and occlusion. In this paper, a comfortable and autonomous swimmer tracking algorithm based on Kalman filter with CamShift is proposed. First, we use the Canny edge detection and inter-frame difference method to segment the entire region of the moving swimmer and then use the extracted target region to initialize the CamShift algorithm. The modified search window supports the automatic tracking of the swimmer. When there is similar color interference in the background, or the swimmer is severely impeded, an improved algorithm combined the Kalman filter with the CamShift algorithm for tracking the target accurately. Experimental results confirmed that the enhanced algorithm significantly improves the outcomes and it can still follow the object effectively and steadily under severe occlusion, noisy environment and color interference.

关键词： Target tracking Image color analysis Image edge detection Interference Kalman filters Heuristic algorithms

Brain Mapping using a Blockchain Approach

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Brain Mapping using a Blockchain Approach

E-Health and Bioengineering Conference (EHB)

作者： Felix-Constantin Adochiei Radu Ciucu Ioana Raluca Adochiei Florin Ciprian Argatu Bogdan Enache Casian Miron George Seritan Faculty of Electrical Engineering University Politehnica of Bucharest Romania Department of Aircraft Integrated Systems and Mechanics Military Technical Academy "Ferdinand I" Bucharest Romania Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

In this paper we propose a pipeline for brain mapping using a decentralized database with a blockchain architecture. We use a two-layer model for a distributed imaging data ledger capable of version control and authoring augmented data. Our platform is capable of direct volume rendering inside a browser window using WebGL. The data visualization pipeline allows for real-time multi-intensity transfer functions and global opacity of a given imaging recording. The platform is intended as a collaboration tool for multiple imaging laboratories and institutes and aims to help in organizing augmented data and multiple level-of-detail recordings used on a wide array of devices. We believe our platform will aid in brain mapping efforts for classification, measurement and visualization of cortical structures trough the aid of virtual reality and a scalable backend based on a standard blockchain technology.

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Privacy of real-time pricing in smart grid

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arXiv 2019年

作者： GhoddousiBoroujeni, Mahrokh Fay, Dominik Dimitrakakis, Christos Kamgarpour, Maryam Department of Electrical Engineering Sharif University of Technology Iran EECS school KTH Royal Institute of Technology Sweden Computer Science and Engineering faculty Chalmers University of Technology Sweden Automatic Control Laboratory D-ITET ETH Zurich Switzerland

Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates before publishing them to ensure users' privacy. Last, the proposed algorithm is tested on data inspired by household occupancy models and its performance is compared to an alternative solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric power utilization