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Improving Intention Detection in single-trial classification Through Fusion of EEG and Eye-Tracker Data

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2023年第1期53卷 132-141页

作者： Ge, Xianliang Pan, Yunxian Wang, Sujie Qian, Linze Yuan, Jingjia Xu, Jie Thakor, Nitish Sun, Yu Zhejiang Univ Ctr Psychol Sci Hangzhou 310027 Peoples R China Zhejiang Univ Minist Educ China Dept Biomed Engn Key Lab Biomed Engn Zhejiang 310027 Peoples R China Johns Hopkins Univ Sch Med Dept Biomed Engn Minist Educ ChinaKey Lab Biomed Engn Baltimore MD 21218 USA Natl Univ Singapore Dept Biomed Engn Minist Educ China Key Lab Biomed Engn Singapore 117456 Singapore Zhejiang Univ SirRun Run Shaw Hosp Dept Neurol Sch Med Hangzhou 310027 Peoples R China Zhejiang Lab Hangzhou 310027 Peoples R China

Intention decoding is an indispensable procedure in hands-free human-computer interaction (HCI). A conventional eye-tracker system using a single-model fixation duration may issue commands that ignore users' real expectations. Here, an eye-brain hybrid brain-computer interface (BCI) interaction system was introduced for intention detection through the fusion of multimodal eye-tracker and event-related potential (ERP) [a measurement derived from electroencephalography (EEG)] features. Eye-tracking and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye tracking and ERP were extracted. Five previously-validated linear discriminant analysis (LDA)-based classifiers [including regularized LDA, stepwise LDA, Bayesian LDA, shrinkage linear discriminant analysis (SKLDA), and spatial-temporal discriminant analysis] and the widely-used convolutional neural network (CNN) method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and the optimal approach was evaluated by modulating the training set and system response duration. Our study demonstrated that the input of multimodal eye tracking and ERP features achieved a superior performance of intention detection in the single-trial classification of active search tasks. Compared with the single-model ERP feature, this new strategy also induced congruent accuracy across classifiers. Moreover, in comparison with other classification methods, we found that SKLDA exhibited a superior performance when fusing features in offline tests (ACC = 0.8783, AUC = 0.9004) and online simulations with various sample amounts and duration lengths. In summary, this study revealed a novel and effective approach for intention classification using an eye-brain hybrid BCI and further supported the real-life application of hands-free HCI in a more precise and stable mann

关键词： Electroencephalography (EEG) event-related potential (ERP) eye-brain-computer interface eye-tracker single-trial classification

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Inter-individual single-trial classification of MEG data using M-CCA

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NEUROIMAGE 2023年第1期273卷 120079-120079页

作者： Michalke, Leo Dreyer, Alexander M. Borst, Jelmer P. Rieger, Jochem W. Carl von Ossietzky Univ Oldenburg Dept Psychol Appl Neurocognit Psychol D-26129 Oldenburg Germany Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intellig NL-9747 AG Groningen Netherlands Carl von Ossietzky Univ Oldenburg Cluster Excellence Hearing4all D-26129 Oldenburg Germany

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires align-ment of recordings across participants. A naive solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional dif-ferences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-subject alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter -subject variability of sensor locations over the brain due to the use of a fixed helmet. Hence, an approach to combine MEG data over individual brains should relax the assumptions that a) brain anatomy and function are tightly linked and b) that the same sensors capture functionally comparable brain activation across individuals. Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activa-tions recorded from 15 participants performing a grasping task. The M-CCA algorithm was applied to transform the data of a set of multiple participants into a common space with maximum correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this com-mon representation. This makes it useful for applications that require transfer of models derived from a group of individuals to new individuals. We demonstrate the usefulness and superiority of the approach with respect to previously used approaches. Finally, we show that our approach requires only a small number of labeled data from the new participant. The proposed method demonstrates that functionally motivated common spaces have potential applications in reducing training time of online brain-computer interfaces, where models can be pre-trained on previous participants/sessions. Moreover, inter-subject alignment via M-CCA has the potential for combining data o

关键词： Magnetoencephalography Multiset CCA Inter-subject alignment single-trial classification Brain machine interfacing Domain adaptation

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Evaluation of Visual Parameters to Control a Visual ERP-BCI Under single-trial classification 17th

Evaluation of Visual Parameters to Control a Visual ERP-BCI ...

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17th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Fernandez-Rodriguez, Alvaro Ron-Angevin, Ricardo Velasco-Alvarez, Francisco Diaz-Pineda, Jaime Letouze, Theodore Andre, Jean-Marc Univ Malaga Inst Univ Invest Telecomunicac Univ Malaga TELMA Dept Tecnol Elect Malaga 29071 Spain Thales Avion France Bordeaux France Bordeaux Univ INP Bordeaux ENSC Lab IMS UMR CNRS 5218 F-33400 Talence France

ISBN: (纸本)9783031430770;9783031430787

A brain-computer interface (BCIs) based on event-related potentials (ERPs) is a technology that provides a communication channel between a device and a user through their brain activity. These systems could be used to assist and facilitate decision making in applications such as an air traffic controller (ATC). Thus, this work attempts to be an approximation to determine whether it is possible to detect the stimulus through a single presentation of a stimulus (single-trial classification) and furthermore, to evaluate the effects of the type of stimulus to be detected, or not knowing the position of the stimulus appearance in an ERP-BCI. This experiment has involved six participants in four experimental conditions. Two conditions varied only in the type of stimulus used, faces (a type of stimulus that has shown high performance in previous ERP-BCI proposals) versus radar planes;and two conditions varied in the prior knowledge of where the stimulus would appear on the screen (knowing vs. not knowing). The results suggest that the use of single-trial classification could be adequate to correctly detect the desired stimulus using and ERP-BCI. In addition, the results reveal no significant effect on either of the two factors. Therefore, it seems that radar planes may be as suitable stimuli as faces and that not knowing the location of the target stimulus is not a significant problem, at least in a standard BCI scenario without distracting stimuli. Therefore, future studies should consider these findings for the design of an ATC using an ERP-BCI for stimulus detection.

关键词： Brain-Computer Interface (BCI) Event-Related Potential (ERP) single-trial classification Air Traffic Controller (ATC)

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Discriminative Canonical Pattern Matching for single-trial classification of ERP Components

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第8期67卷 2266-2275页

作者： Xiao, Xiaolin Xu, Minpeng Jin, Jing Wang, Yijun Jung, Tzyy-Ping Ming, Dong Tianjin Univ Coll Precis Instruments & Optoelect Engn Dept Biomed Engn Lab Neural Engn & Rehabil Tianjin 300072 Peoples R China Tianjin Univ Acad Med Engn & Translat Med Tianjin Int Joint Res Ctr Neural Engn Tianjin 300072 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai Peoples R China Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China Univ Calif Swartz Ctr Computat Neurosci La Jolla CA USA

Event-related potentials (ERPs) are one of the most popular control signals for brain-computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components.

关键词： Electroencephalography Band-pass filters Electrodes Visualization classification algorithms Training Electric potential Brain-computer interface (BCI) electroencephalogram (EEG) single-trial classification event-related potential (ERP) discriminative canonical pattern matching (DCPM)

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Multilinear Discriminant Analysis With Subspace Constraints for single-trial classification of Event-Related Potentials

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2016年第7期10卷 1295-1305页

作者： Higashi, Hiroshi Rutkowski, Tomasz M. Tanaka, Toshihisa Tanaka, Yuichi Toyohashi Univ Technol Grad Sch Engn Toyohashi Aichi 4418580 Japan Univ Tokyo Grad Sch Educ Tokyo 1138654 Japan RIKEN Brain Sci Inst Saitama 3510106 Japan Tokyo Univ Agr & Technol Inst Engn Tokyo 1830057 Japan

The classification accuracy of a brain-computer interface (BCI) frequently suffers from ill-posed and overfitting problems. To avoid and alleviate these problems, we propose a method of a multilinear discriminant analysis with constraints to augment parameter reduction, regularization, and additional prior information for event-related potential (ERP)-based BCIs. The method reduces the number of parameters by multilinearization, regularizes the ill-posedness via subspaces that constrain the parameter spaces, and incorporates a brain functional connectivity through the constraints. The experimental results show that the proposed method improved the classification accuracy rates in a single-trial ERP processing.

关键词： Brain-computer/machine interface (BCI/BMI) event-related potentials electroencephalogram (EEG) single-trial classification linear discriminant analysis multilinear algebra

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A single-trial Multiclass classification of Various Motor Imagery Tasks for EEG-Based Brain-Computer Interface Communication

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2017年第1期100卷 18-26页

作者： Misawa, Tadanobu Matsuda, Jumpei Hirobayashi, Shigeki Toyama Univ Grad Sch Sci & Engn Toyama Japan Toyama Univ Grad Sch Sci & Engn Educ Toyama Japan

We studied the brain activity (alpha and beta rhythms) with various motor imagery tasks for improvement of brain-computer interface (BCI) usability using 14 electroencephalography (EEG) electrodes in five healthy subjects. For this purpose, we estimated two-class and four-class classification accuracy on the EEG signals with four motor imagery tasks derived from each type motor imagery (three classical motor imagery and one proposed mental strategy) task using t-test and super vector machine. The proposed mental strategy was imagery writing Kanji (Japanese characters). It has the possibility of both sensorimotor cortex and the visual cortex activation. Therefore, we expected to extract the distinct activity different from the activation with classical motor imagery tasks. In the two-class classification results, the classification accuracy was 73.7% on average in all combination of derived motor imagery task. Moreover, we demonstrated that four-class classification accuracy was 40.1% and the proposed task had possibility of the visual cortex activation dominantly. In experimental results, we proposed the new way for improvement of BCI application usability.

关键词： electroencephalography (EEG) brain-computer interface (BCI) motor imagery (MI) single-trial classification usability Kanji (Chinese characters)

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Optimizing single-trial EEG classification by Stationary Matrix Logistic Regression in Brain-Computer Interface

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2016年第11期27卷 2301-2313页

作者： Zeng, Hong Song, Aiguo Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Child Dev & Learning Sci Nanjing 210096 Jiangsu Peoples R China

In addition to the noisy and limited spatial resolution characteristics of the electroencephalography (EEG) signal, the intrinsic nonstationarity in the EEG data makes the single-trial EEG classification an even more challenging problem in brain-computer interface (BCI). Variations of the signal properties within a session often result in deteriorated classification performance. This is mainly attributed to the reason that the routine feature extraction or classification method does not take the changes in the signal into account. Although several extensions to the standard feature extraction method have been proposed to reduce the sensitivity to nonstationarity in data, they optimize different objective functions from that of the subsequent classification model, and thereby, the extracted features may not be optimized for the classification. In this paper, we propose an approach that directly optimizes the classifier's discriminativity and robustness against the within-session nonstationarity of the EEG data through a single optimization paradigm, and show that it can greatly improve the performance, in particular for the subjects who have difficulty in controlling a BCI. Moreover, the experimental results on two benchmark data sets demonstrate that our approach significantly outperforms the compared approaches in reducing classification error rates.

关键词： Accelerated proximal gradient (APG) brain-computer interface (BCI) matrix logistic regression (MLR) single-trial classification stationarity regularizer

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Transfer Learning and Active Transfer Learning for Reducing Calibration Data in single-trial classification of Visually-Evoked Potentials

Transfer Learning and Active Transfer Learning for Reducing ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wu, Dongrui Lance, Brent Lawhern, Vernon GE Global Res Machine Learning Lab Niskayuna NY USA US Army Res Lab Translat Neurosci Branch Adelphi MD USA Univ Texas San Antonio Dept Comp Sci San Antonio TX USA DCS Corp Knowledge Serv Branch Alexandria VA USA

ISBN: (纸本)9781479938407

single-trial Event-Related Potential (ERP) classification is a key requirement for several types of Brain-Computer Interaction (BCI) technologies. However, strong individual differences make it challenging to develop a generic single-trial ERP classifier that performs well for all subjects. Usually some subject-specific training samples need to be collected in an initial calibration session to customize the classifier. However, if implemented into an actual BCI system, then this calibration process would decrease the utility of the system, potentially decreasing its usability. In this paper we propose a Transfer Learning approach for reducing the amount of subject-specific data in online single-trial ERP classifier calibration, and an Active Transfer Learning approach for offline calibration. By applying these approaches to data from a Visually-Evoked Potential EEG experiment, we demonstrate that they improve the classification performance, given the same number of labeled subject-specific training samples. In other words, these approaches can also attain a desired level of classification accuracy with less labeling effort when compared to a randomly selected training set.

关键词： single-trial classification ERP VEP EEG transfer learning active learning active transfer learning

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Transfer Learning and Active Transfer Learning for Reducing Calibration Data in single-trial classification of Visually-Evoked Potentials

Transfer Learning and Active Transfer Learning for Reducing ...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Dongrui Wu Brent Lance Vernon Lawhern Machine Learning Laboratory GE Global Research Translational Neuroscience Branch U.S. Army Research Laboratory Department of Computer Science University of Texas at San Antonio

ISBN: (纸本)9781479938414

关键词： single-trial classification ERP VEP EEG Transfer learning Active learning Active transfer learning

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Individualized real-time prediction of working memory performance by classifying electroencephalography signals

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022年第2期32卷 575-589页

作者： Mirjalili, Mina Zomorrodi, Reza Daskalakis, Zafiris J. Hill, Sean Rajji, Tarek K. Ctr Addict & Mental Hlth Campbell Family Mental Hlth Res Inst Toronto ON Canada Ctr Addict & Mental Hlth Adult Neurodev & Geriatr Psychiat Div 80 Workman Way Toronto ON M6J 1H4 Canada Univ Toronto Inst Med Sci Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada Ctr Addict & Mental Hlth Temerty Ctr Therapeut Brain Intervent Toronto ON Canada Univ Calif San Diego Hlth Sch Med Dept Psychiat La Jolla CA USA Ctr Addict & Mental Hlth Krembil Ctr Neuroinformat Toronto ON Canada Univ Toronto Dept Psychiat Toronto ON Canada Univ Toronto Toronto Dementia Res Alliance Toronto ON Canada

An individualized model that predicts trial-by-trial working memory performance is instrumental for personalized interventions. Here, we propose a single-trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non-correct during a working memory task, N-back. We used event-related (de-)synchronization (ERD and ERS) prior to an anticipatory cue as features. The proposed comprehensive process addresses single-trial EEG classification challenges such as temporal overlap between training and testing datasets, feature selection's stability, and significance of the classification accuracy which have been often overlooked. Our model identified for the first time a few (ranged between 4 and 10) brain regions and oscillations where ERD and ERS predicted an individual's performance. Mean (SD) prediction accuracy across 50 participants (mean age [SD] = 28.56 [7.55]) was 69.51% (8.41). Accuracy was significantly above chance in 34 participants. This machine learning-based approach provides a proof of principle for individualizing EEG targets for potential interventions.

关键词： EEG event-related (de-)synchronization N-back single-trial classification working memory

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