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EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based brain-computer interfaces

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第12期28卷 2773-2782页

作者： Santamaria-Vazquez, Eduardo Martinez-Cagigal, Victor Vaquerizo-Villar, Fernando Hornero, Roberto Univ Valladolid ETS Ingenieros Telecomunicac Biomed Engn Grp GIB Valladolid 47011 Spain Inst Salud Carlos III CIBER BBN Madrid 28029 Spain

In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.

关键词： Electroencephalography brain modeling computer architecture Visualization Training Convolutional neural networks Convolution brain-computer interfaces event-related potentials P300 deep learning convolutional neural networks inception transfer learning

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Adversarial filtering based evasion and backdoor attacks to EEG-based brain-computer interfaces

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INFORMATION FUSION 2024年 107卷

作者： Meng, Lubin Jiang, Xue Chen, Xiaoqing Liu, Wenzhong Luo, Hanbin Wu, Dongrui Huazhong Univ Sci & Technol Belt & Rd Joint Lab Measurement & Control Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs.

关键词： brain-computer interfaces Machine learning Adversarial attack Adversarial filtering

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Evidences of cognitive effects over auditory steady-state responses by means of artificial neural networks and its use in brain-computer interfaces

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NEUROCOMPUTING 2009年第16-18期72卷 3617-3623页

作者： Lopez, M. -A. Pomares, Hector Pelayo, Francisco Urquiza, Jose Perez, Javier Univ Granada Dept Comp Architecture Technol Spanish Minist Educ E-18071 Granada Spain

The auditory steady-state response is an EEG potential elicited by the repetitive presentation of auditory stimuli. Researchers have found contradictory results about the influence of cognitive tasks, such as the selective attention, over this potential. It has been proved that selective attention is able to modulate cortex originated steady-state responses, such as the visual. This fact has been widely used to develop brain-computer interfaces. However for complete locked-in patients, such as those in an advanced state of Amyotrophic lateral sclerosis, visual stimuli are not longer suitable, hence the need of another type of stimulus, generally auditory, for both stimulation and feedback. In this paper we present a study based on artificial neural networks that evidences the effects of selective attention over auditory steady-state responses and the use in brain-computer interfaces is discussed. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Self-organizing map brain-computer interfaces Selective attention Auditory steady-state response

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Manifold Embedded Knowledge Transfer for brain-computer interfaces

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第5期28卷 1117-1127页

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

Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject electroencephalogram (EEG) classification, i.e., we have labeled EEG trials from one or more source subjects, but only unlabeled EEG trials from the target subject. We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between the source and the target domains, while preserving their geometric structures. MEKT can cope with one or multiple source domains, and can be computed efficiently. We also propose a domain transferability estimation (DTE) approach to identify the most beneficial source domains, in case there are a large number of source domains. Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.

关键词： Covariance matrices Electroencephalography Manifolds Feature extraction Task analysis Geometry Symmetric matrices brain-computer interfaces electroencephalogram Riemannian manifold transfer learning

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Optimizing the number of electrodes and spatial filters for brain-computer interfaces by means of an evolutionary multi-objective approach

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第15-16期42卷 6215-6223页

作者： Aler, Ricardo Galvan, Ines M. Univ Carlos III Madrid Leganes 28911 Spain

Obtaining high accuracy classification from brain computer interfaces require to attach many electrodes on the scalp of subjects. On the other hand, their placement on the scalp involves generally a laborious and time consuming process. Therefore, it is important for the practitioner to estimate how many electrodes, and which ones, are needed to obtain the required accuracy. With this purpose, a multi-objective formulation is proposed in order to obtain a set of solutions (Pareto front) that represent all the optimal tradeoffs between the number of channels and the classification accuracy, from where the practitioner can choose. Additionally, previous research has shown that classification accuracy highly depends on the proper tuning of the filter used to preprocess the electroencephalogram. Therefore, in this work, the Non-dominated Sorting Genetic Algorithm II is used for optimizing both the number of electrodes and the classification error, through the optimization of a spatial filter encoded in the solution. The fact that the filter is part of the solution allows to determine which electrodes are to be selected by using a simple threshold, instead of a long binary mask as in other approaches. Empirical results show that indeed, the simultaneous optimization of the spatial filter and selected electrodes is crucial to obtain a low classification error, compared to other approaches that reduce the number of electrodes but do not modify the filter. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： brain-computer interfaces Multi-objective optimization Electrode selection Spatial filters

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Learning Discriminative Spatiospectral Features of ERPs for Accurate brain-computer interfaces

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2019年第5期23卷 2009-2020页

作者： Abibullaev, Berdakh Zollanvari, Amin Nazarbayev Univ Dept Robot & Mech Astana 010000 Kazakhstan Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan

Constructing accurate predictive models is at the heart of brain-computer interfaces (BCIs) because these models can ultimately translate brain activities into communication and control commands. The majority of the previous work in BCI use spatial, temporal, or spatiotemporal features of event-related potentials (ERPs). In this study, we examined the discriminatory effect of their spatiospectral features to capture the most relevant set of neural activities from electroencephalographic recordings that represent users' mental intent. In this regard, we model ERP waveforms using a sum of sinusoids with unknown amplitudes, frequencies, and phases. The effect of this signal modeling step is to represent high-dimensional ERP waveforms in a substantially lower dimensionality space, which includes their dominant power spectral contents. We found that the most discriminative frequencies for accurate decoding of visual attention modulated ERPs lie in a spectral range less than 6.4 Hz. This was empirically verified by treating dominant frequency contents of ERP waveforms as feature vectors in the state-of-the-art machine learning techniques used herein. The constructed predictive models achieved remarkable performance, which for some subjects was as high as 94% as measured by the area under curve. Using these spectral contents, we further studied the discriminatory effect of each channel and proposed an efficient strategy to choose subject-specific subsets of channels that generally led to classifiers with comparable performance.

关键词： brain-computer interfaces ERPs P300 EEG signal processing machine learning

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Transfer Learning Based on Hybrid Riemannian and Euclidean Space Data Alignment and Subject Selection in brain-computer interfaces

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IEEE ACCESS 2021年 9卷 6201-6212页

作者： Li, Yan Wei, Qingguo Chen, Yuebin Zhou, Xichen Nanchang Univ Sch Informat Engn Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China

Transfer learning is a promising approach for reducing training time in a brain-computer interface (BCI). However, how to effectively transfer data from previous users to a new user poses a huge challenge. This paper presents a novel transfer learning approach that combines data alignment and source subject selection for motor imagery (MI) based BCIs. The former is achieved by a reference matrix from the regularization of the two reference matrices estimated in Riemannian and Euclidean space respectively, whereas the latter is implemented by a modified sequential forward floating-point search algorithm. The aligned training data from chosen source subjects are used for creating a classification model based on either spatial covariance matrices in Riemannian space or common spatial pattern algorithm in Euclidean space. The proposed algorithms were evaluated on two MI based BCI data sets with different subjects and compared with existing transfer learning algorithms with sole data alignment or subject selection. The experimental results show that the hybrid-space data alignment methods for reducing the differences among subjects significantly outperform two single-space alignment methods, and the source subject selection method can substantially enhance the similarity between source subjects and the target subject. The combination of the two methods achieves superior classification performance compared to either one. The proposed algorithms will greatly facilitate the real-world applications of MI based BCIs.

关键词： brain-computer interfaces transfer learning data alignment hybrid Riemannian and Euclidean space data alignment source subject selection

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Transfer Learning Based on Optimal Transport for Motor Imagery brain-computer interfaces

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第2期69卷 807-817页

作者： Peterson, Victoria Nieto, Nicolas Wyser, Dominik Lambercy, Olivier Gassert, Roger Milone, Diego H. Spies, Ruben D. UNL CONICET IMAL Inst Matemat Aplicada Litoral RA-3000 Santa Fe Argentina UNL CONICET Inst Invest Senales Sistemas & Inteligencia Compu FICHSinc I Santa Fe Argentina Swiss Fed Inst Technol Dept Hlth Sci & Technol Rehabil Engn Lab Zurich Switzerland

Objective: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use. Methods: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used. Results: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods. Conclusions: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs. Significance: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation.

关键词： Testing Electroencephalography Calibration brain modeling Decoding Transfer learning Task analysis brain-computer interfaces domain adaptation motor imagery optimal transport transfer learning

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A Systematic Deep Learning Model Selection for P300-Based brain-computer interfaces

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2744-2756页

作者： Abibullaev, Berdakh Zollanvari, Amin Nazarbayev Univ Sch Engn & Digital Sci Dept Robot & Mechatron Nur Sultan 010000 Kazakhstan Nazarbayev Univ Sch Engn & Digital Sci Dept Elect & Comp Engn Nur Sultan 010000 Kazakhstan

Predicting attention-modulated brain responses is a major area of investigation in brain-computer interface (BCI) research that aims to translate neural activities into useful control and communication commands. Such studies involve collecting electroencephalographic (EEG) data from subjects to train classifiers for decoding users' mental states. However, various sources of inter or intrasubject variabilities in brain signals render training classifiers in BCI systems challenging. From a machine learning perspective, this model training generally follows a common methodology: 1) apply some type of feature extraction, which can be time-consuming and may require domain knowledge and 2) train a classifier using extracted features. The advent of deep learning technologies has offered unprecedented opportunities to not only construct remarkably accurate classifiers but also to integrate the feature extraction stage into the classifier construction. Although integrating feature extraction, which is generally domain-dependent, into the classifier construction is a considerable advantage of deep learning models, the process of architecture selection for BCIs generally depends on domain knowledge. In this study, we examine the feasibility of conducting a systematic model selection combined with mainstream deep learning architectures to construct accurate classifiers for decoding P300 event-related potentials. In particular, we present the results of 232 convolutional neural networks (CNNs) (4 datasets x 58 structures), 36 long short-term memory cells (LSTMs) (4 datasets x 9 structures), and 320 hybrid CNN-LSTM models (4 datasets x 80 structures) of varying complexity. Our empirical results show that in the classification of P300 waveforms, the constructed predictive models can outperform the current state-of-the-art deep learning architectures, which are partially or entirely inspired by domain knowledge. The source codes and constructed models are available at https://githu

关键词： brain modeling Deep learning Feature extraction Electroencephalography Visualization Electrodes Decoding brain-computer interfaces convolutional neural network (CNN) deep learning event-related potential (ERP) long short-term memory unit model selection P300 waves

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Detection of Subconscious Face Recognition Using Consumer-Grade brain-computer interfaces

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ACM TRANSACTIONS ON APPLIED PERCEPTION 2016年第1期14卷 1–20页

作者： Martin, Miguel Vargas Cho, Victor Aversano, Gabriel Univ Ontario Inst Technol Oshawa ON Canada

We test the possibility of tapping the subconscious mind for face recognition using consumer-grade BCIs. To this end, we performed an experiment whereby subjects were presented with photographs of famous persons with the expectation that about 20% of them would be (consciously) recognized;and since the photos are of famous persons, we expected that subjects would have seen before some of the 80% they didn't (consciously) recognize. Further, we expected that their subconscious would have recognized some of those in the 80% pool that they had seen before. An exit questionnaire and a set of criteria allowed us to label recognitions as conscious, false, no recognitions, or subconscious recognitions. We analyzed a number of event related potentials training and testing a support vector machine. We found that our method is capable of differentiating between no recognitions and subconscious recognitions with promising accuracy levels, suggesting that tapping the subconscious mind for face recognition is feasible.

关键词： Subconscious mind face recognition brain-computer interfaces

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