scholarly journals Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141984086 ◽  
Author(s):  
Chuanqi Tan ◽  
Fuchun Sun ◽  
Bin Fang ◽  
Tao Kong ◽  
Wenchang Zhang
Keyword(s):  
Transfer Learning ◽  
Negative Transfer ◽  
Brain Computer Interface ◽  
Training Data ◽  
Computer Interface ◽  
Rehabilitation Robot ◽  
Target Domain ◽  
Source Domain ◽  
Adversarial Network ◽  
The Brain

The brain–computer interface-based rehabilitation robot has quickly become a very important research area due to its natural interaction. One of the most important problems in brain–computer interface is that large-scale annotated electroencephalography data sets required by advanced classifiers are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed with the test data. It can be considered a powerful tool for solving the problem of insufficient training data. There are two basic issues with transfer learning, under transfer and negative transfer. We proposed a novel brain–computer interface framework by using autoencoder-based transfer learning, which includes three main components: an autoencoder framework, a joint adversarial network, and a regularized manifold constraint. The autoencoder framework automatically encodes and reconstructs data from source and target domains and forces the neural network to learn to represent these domains reliably. The joint adversarial network aims to force the network to learn to encode more appropriately for the source domain and target domain simultaneously, thereby overcoming the problem of under transfer. The regularized manifold constraint aims to avoid the problem of negative transfer by avoiding geometric manifold structure in the target domain being destroyed by the source domain. Experiments show that the brain–computer interface framework proposed by us can achieve better results than state-of-the-art approaches in electroencephalography signal classification tasks. This is helpful in aiding our rehabilitation robot to understand the intention of patients and can help patients to carry out rehabilitation exercises effectively.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

scholarly journals Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

2019 ◽  
Vol 29 (10) ◽  
pp. 1950025 ◽  
Author(s):  
Pramod Gaur ◽  
Karl McCreadie ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Classification Model ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Subject Specific

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ibrahim Hossain ◽  
Abbas Khosravi ◽  
Imali Hettiarachchi ◽  
Saeid Nahavandi
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Superior Performance ◽  
Computer Interface ◽  
Imagery Task ◽  
Learning Framework ◽  
Optimal Ensemble

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.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

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