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Transfer Learning for brain-computer interfaces: A Euclidean Space Data Alignment Approach

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第2期67卷 399-410页

作者： He, He Wu, Dongrui Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials;second, its computational cost is very low;and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.

关键词： Covariance matrices Electroencephalography Task analysis Signal processing Machine learning algorithms Microsoft Windows brain-computer interfaces brain-computer interface data alignment EEG Riemannian geometry transfer learning

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A Flexible, Robust, and Gel-Free Electroencephalogram Electrode for Noninvasive brain-computer interfaces

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NANO LETTERS 2019年第10期19卷 6853-6861页

作者： Lin, Sen Liu, Junchen Li, Wenzheng Wang, Dong Huang, Ya Jia, Chao Li, Ziwei Murtaza, Muhammad Wang, Haiyang Song, Jianan Liu, Zhenglian Huang, Kai Zu, Di Lei, Ming Hong, Bo Wu, Hui Tsinghua Univ Sch Mat Sci & Engn State Key Lab New Ceram & Fine Proc Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China

brain-computer interfaces (BCIs) enable direct and near-instant communication between the brain and electronic devices. One of the biggest remaining challenges is to develop an effective noninvasive BCI that allows the recording electrodes to avoid hair on human skin without the inconveniences and complications of using a conductive gel. In this study, we developed a cost-effective, easily manufacturable, flexible, robust, and gel-free silver nanowire/polyvinyl butyral (PVB)/melamine sponge (AgPMS) electroencephalogram (EEG) electrode that circumvents problems with hair. Because of surface metallization by the silver nanowires (AgNWs), the sponge has a high conductivity of 917 S/m while its weight remains the same. The flexible sponge framework and self-locking AgNWs combine to give the new electrode remarkable mechanical stability (the conductivity remains unchanged after 10 000 cycles at 10% compression) and the ability to bypass hair. A BCI application based on steady-state visual evoked potential (SSVEP) measurements on hairless skin shows that the BCI accuracy of the new electrode (86%) is approximately the same as that of conventional electrodes supported by a conductive gel (88%). Most importantly, the performance of the AgPMS on hairy skin is not significantly reduced, which indicates that the new electrode can replace conventional electrodes for both hairless and hairy skin BCIs and other EEG applications.

关键词： brain-computer interfaces flexible electronics silver nanowire gel-free electrode

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Adding Human Learning in brain-computer interfaces (BCIs): Towards a Practical Control Modality

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ACM TRANSACTIONS ON computer-HUMAN INTERACTION 2015年第3期22卷 1–37页

作者： Kosmyna, Nataliya Tarpin-Bernard, Franck Rivet, Bertrand IIHM LIG Grenoble 9 France Sci Brain Training F-69603 Villeurbanne France Gipsa Lab Grenoble INP F-38400 St Martin Dheres France

In this article, we introduce CLBCI (Co-Learning for brain-computer interfaces), a BCI architecture based on co-learning in which users can give explicit feedback to the system rather than just receiving feedback. CLBCI is based on minimum distance classification with Independent Component Analysis (ICA) and allows for shorter training times compared to classical BCIs, as well as faster learning in users and a good performance progression. We further propose a new scheme for real-time two-dimensional visualization of classification outcomes using Wachspress coordinate interpolation. It allows us to represent classification outcomes for n classes in simple regular polygons. Our objective is to devise a BCI system that constitutes a practical interaction modality that can be deployed rapidly and used on a regular basis. We apply our system to an event-based control task in the form of a simple shooter game in which we evaluate the learning effect induced by our architecture compared to a classical approach. We also evaluate how much user feedback and our visualization method contribute to the performance of the system.

关键词： Co-learning brain-computer interfaces Visualization Wachspress coordinates interactive machine learning

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A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain-computer interfaces

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SOFT COMPUTING 2015年第4期19卷 1019-1035页

作者： Alhaddad, Mohammed J. Mohammed, Ahmed Kamel, Mahmoud Hagras, Hani King Abdulaziz Univ Jeddah 21413 Saudi Arabia Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

One of the important areas of brain-computer interface (BCI) research is to identify event-related potentials (ERPs) which are spatial-temporal patterns of the brain activity that happen after presentation of a stimulus and before execution of a movement. One of the important ERPs is the P300 which is an endogenous component of ERPs with a latency of about 300 ms which is elicited by significant stimuli (visual, or auditory). Various machine learning-based classifiers have been used to predict the P300 events and relate them to the human intended activities. However, the vast majority of the employed techniques like Bayesian linear discriminant analysis (BLDA) and regularized fisher linear discriminant analysis (RFLDA) are black box models which are difficult to understand and analyse by a normal clinician. In addition, due to the inter- and intra-user uncertainties associated with the P300 events, most of the existing classifiers need to be trained for a specific user under specific circumstances and the classifier needs to be retrained for different users or change of circumstances. In this paper, we present an interval type-2 fuzzy logic-based classifier which is able to handle the users' uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximise the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. We will present various experiments which were performed on standard data sets and using real-data sets obtained from real subjects' experiments performed in the BCI laboratory in King Abdulaziz University. It wi

关键词： Type-2 fuzzy logic systems Linguistic model generation Modeling perceptions brain-computer interfaces

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Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning

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MEDICAL ENGINEERING & PHYSICS 2013年第12期35卷 1811-1818页

作者： Abibullaev, Berdakh An, Jinung Jin, Sang-Hyeon Lee, Seung Hyun Moon, Jeon Il Nazarbayev Univ Sch Sci & Technol Dept Robot Astana 010000 Kazakhstan Daegu Gyeongbuk Inst Sci & Technol Taegu 711873 South Korea

brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects. (C) 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： brain-computer interfaces Functional near-infrared spectroscopy Inter-subject variability Support vector machines RKHS Multiple kernel learning

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Human Neuro-Activity for Securing Body Area Networks: Application of brain-computer interfaces to People-Centric Internet of Things

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IEEE COMMUNICATIONS MAGAZINE 2017年第2期55卷 62-67页

作者： Valenzuela-Valdes, Juan F. Angel Lopez, Miguel Padilla, Pablo Padilla, Jose L. Minguillon, Jesus Univ Granada Granada Spain Univ Granada Dept Signal Theory Telemat & Commun Granada Spain Univ Granada Marie Curie Programme Granada Spain Univ Granada Dept Comp Architecture & Technol Granada Spain Ecole Polytech Fed Lausanne Nanoelect Devices Lab Lausanne Switzerland

A former definition states that a brain corn puter Interface provides a direct communication channel to the brain without the need for muscles and nerves. With the emergence of wearable and wireless brain-computer interlaces, these systems have evolved to become part of wireless body area networks, offering people-centric applications such as cognitive workload assessment and detection of selective attention. Currently, wireless body area networks are mostly integrated by low-cost devices that, because of their limited hardware resources, cannot generate secure random numbers for encryption. This is a critical issue in the context of new Internet of Things device communication and its security. Such devices require securing their communication, mostly by means of the automatic renewal of the cryptographic keys. In the domain of the people-centric Internet of Things, we propose to use wireless brain-computer interfaces as a secure source of entropy, based on neuro-activity, capable to generate secure keys that outperforms other generation methods. In our approach, current wireless brain-computer interface technology is an attractive option to offer novel services emerged from novel necessities in the context of the people-centric Internet of Things. Our proposal is an implementation of the human-in-the-loop paradigm, in which devices and humans indistinctly request and offer services to each other for mutual benefit.

关键词： brain-computer interfaces Internet of things Body area networks Neurophysiology Human factors

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Transfer Learning in brain-computer interfaces

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2016年第1期11卷 20-31页

作者： Jayaram, Vinay Alamgir, Morteza Altun, Yasemin Schoelkopf, Bernhard Grosse-Wentrup, Moritz Univ Tubingen IMPRS Cognit & Syst Neurosci Tubingen Germany Max Planck Inst Intelligent Syst Dept Empir Inference Tubingen Germany Univ Hamburg Dept Comp Sci Schluterstr 70 Hamburg Germany

The performance of brain-computer interfaces (BCIs) improves with the amount of available training data; the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.

关键词： Training data brain modeling Data models brain-computer interfaces Spatial filters Electroencephalography Machine learning

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Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces

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BIOMEDICAL ENGINEERING ONLINE 2014年第1期13卷 1-13页

作者： Cao, Teng Wan, Feng Wong, Chi Man da Cruz, Janir Nuno Hu, Yong Univ Macau Dept Elect & Comp Engn Macau Peoples R China Univ Hong Kong Dept Orthopaed & Traumatol Pokfulam Hong Kong Peoples R China

Background: The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users' discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs. Methods: How the EEG indices (amplitudes in delta, theta, alpha and beta frequency bands), the selected ratio indices (0/alpha and (0 + alpha)/beta), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index. Results: Consistent results are obtained in two experiments: the significant increases in alpha and (theta + alpha)/beta, as well as the decrease in theta/alpha are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users' fatigue. Conclusions: The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based

关键词： Fatigue Objective evaluation brain-computer interfaces Steady-state visual evoked potential Electroencephalography spectral analysis

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Single-Trial NIRS Data Classification for brain-computer interfaces Using Graph Signal Processing

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2018年第9期26卷 1700-1709页

作者： Petrantonakis, Panagiotis C. Kompatsiaris, Ioannis Ctr Res & Technol Hellas Inst Informat Technol Thessaloniki 57001 Greece

Near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) systems use feature extraction methods relying mainly on the slope characteristics and mean changes of the hemodynamic responses in respect to certain mental tasks. Nevertheless, spatial patterns across the measurement channels have been detected and should be considered during the feature vector extraction stage of the BCI realization. In this paper, a graph signal processing (GSP) approach for feature extraction is adopted in order to capture the aforementioned spatial information of the NIRS signals. The proposed GSP-based methodology for feature extraction in NIRS-based BCI systems, namely graph NIRS (GNIRS), is applied on a publicly available dataset of NIRS recordings during a mental arithmetic task. GNIRS exhibits higher classification rates (CRs), up to 92.52%, as compared to the CRs of two state-of-the-art feature extraction methodologies related to slope and mean values of hemodynamic response, i.e., 90.35% and 82.60%, respectively. In addition, GNIRS leads to the formation of feature vectors with reduced dimensionality in comparison with the baseline approaches. Moreover, it is shown to facilitate high CRs even from the first second after the onset of the mental task, paving the way for faster NIRS-based BCI systems.

关键词： brain-computer interfaces graph signal processing mental arithmetic near-infrared spectroscopy

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Stimulus Design for Visual Evoked Potential Based brain-computer interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2545-2551页

作者： Xu, Haoyin Hsu, Sheng-Hsiou Nakanishi, Masaki Lin, Yufan Jung, Tzyy-Ping Cauwenberghs, Gert Univ Calif San Diego Inst Neural Computat Swartz Ctr Computat Neurosci La Jolla CA 92093 USA Univ Calif San Diego Inst Neural Computat Dept Bioengn La Jolla CA 92093 USA

Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.

关键词： brain-computer interfaces electroen-cephalography m-sequence stimulus design visual evoked potentials

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