Active transfer learning and selective instance transfer with active learning for motor imagery based BCI

Hossain, Ibrahim; Khosravi, Abbas; Nahavandhi, Saeid

doi:10.1109/ijcnn.2016.7727726

Cited by 28 publications

(11 citation statements)

References 23 publications

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“…For the first category, it is assumed that the partial source EEG data can be selected and considered together with few labeled target EEG data. The source EEG data are obtained through either instance selection or importance sampling crossvalidation (Li et al, 2010;Hossain et al, 2016Hossain et al, , 2018Zanini et al, 2018). For example, Hossain et al (2016) proposed an instance selection strategy based on active learning.…”

Section: Related Workmentioning

confidence: 99%

“…The source EEG data are obtained through either instance selection or importance sampling crossvalidation (Li et al, 2010;Hossain et al, 2016Hossain et al, , 2018Zanini et al, 2018). For example, Hossain et al (2016) proposed an instance selection strategy based on active learning. The selected source EEG data were then used together with available target-labeled EEG data to train the target model.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

et al. 2020

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In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

et al. 2020

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“…In MI-based BCIs, transfer learning can be applied on either raw EEG, feature or classification domains. The proposed transfer learning algorithms on raw EEG have been mostly based on either importance sampling cross validation [14], [15] or instance selection [16], [17]. For example, a covariate shift adaptation has been proposed in [14], where data from other subjects were weighted based on importance sampling cross-validation.…”

Section: Introductionmentioning

confidence: 99%

“…The parts with high weights were then used to estimate the final prediction function. In [16], [17], an instance selection approach has been proposed based on active learning to select trials that were close to the few informative trials of the new subject. The selected trials were added to the existing labeled trials of the new subject to train the BCI model.…”

Section: Introductionmentioning

confidence: 99%

Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface

Azab

Mihaylova

Ang

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

136

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One of the major limitations of motor imagery (MI)based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this study, a new similarity measure based on the kullback leibler divergence (KL) is used to measure similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared to the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results particularly when few subject-specific trials were available for training (p < 0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms.

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“…The authors used all samples from other subjects directly without any adaptation or selection. An improved version of ATL was proposed and implemented for binary MI-based BCI in our preliminary work [ 37 , 38 ]. Both works implemented ATL on binary classification.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

Hossain

Khosravi

Hettiarachchi

et al. 2018

Computational Intelligence and Neuroscience

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A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.

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Active transfer learning and selective instance transfer with active learning for motor imagery based BCI

Cited by 28 publications

References 23 publications

A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

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