scholarly journals Across-subject offline decoding of motor imagery from MEG and EEG

2018 ◽  
Author(s):  
Hanna-Leena Halme ◽  
Lauri Parkkonen
Keyword(s):  
Logistic Regression ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Computer Interfaces ◽  
The Common ◽  
Calibration Time ◽  
Decoding Accuracy ◽  
And Training ◽  
Healthy Participants ◽  
Passive Movements

AbstractLong calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects’ data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG.Six methods were tested on data involving MEG and EEG measurements of healthy participants. Only subjects with good within-subject accuracies were selected for inter-subject decoding. Three methods were based on the Common Spatial Patterns (CSP) algorithm, and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using 1) MI and 2) passive movements (PM) for training, separately for MEG and EEG.When the classifier was trained by MI, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. When PM were used for training, none of the inter-subject methods yielded above chance level (58.7%) accuracy.In conclusion, MTL and training with other subject’s MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.

scholarly journals Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuanci Zheng ◽  
Jie Li ◽  
Hongfei Ji ◽  
Lili Duan ◽  
Maozhen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Training Model ◽  
Average Accuracy ◽  
Source Data ◽  
Task Transfer ◽  
Calibration Time ◽  
And Training ◽  
Bci System

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

2021 ◽  
pp. 2150030
Author(s):  
Jing Jin ◽  
Hua Fang ◽  
Ian Daly ◽  
Ruocheng Xiao ◽  
Yangyang Miao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Minimum Covariance Determinant ◽  
Fisher Score ◽  
Practical Applications ◽  
Computer Interfaces ◽  
Highly Sensitive ◽  
Feature Optimization ◽  
The Common ◽  
Model Training

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

scholarly journals Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Dieter Devlaminck ◽  
Bart Wyns ◽  
Moritz Grosse-Wentrup ◽  
Georges Otte ◽  
Patrick Santens
Keyword(s):  
Motor Imagery ◽  
Spatial Filter ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Calibration Data ◽  
Common Spatial Pattern ◽  
Computer Interfaces ◽  
Learning Techniques ◽  
The Common

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

scholarly journals Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mengxi Dai ◽  
Dezhi Zheng ◽  
Shucong Liu ◽  
Pengju Zhang
Keyword(s):  
Motor Imagery ◽  
State Of The Art ◽  
Classification Performance ◽  
Training Data ◽  
Common Spatial Pattern ◽  
Computer Interfaces ◽  
Training Samples ◽  
Invariant Kernel ◽  
The Common ◽  
Effectiveness And Efficiency

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.

scholarly journals Simultaneously Spatiospectral Pattern Learning and Contaminated Trial Pruning for Electroencephalography-Based Brain Computer Interface

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1387
Author(s):  
Chun-Ping Shieh ◽  
Shih-Hung Yang ◽  
Yu-Shun Liu ◽  
Yun-Ting Kuo ◽  
Yu-Chun Lo ◽  
...  
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Conditional Probability Density ◽  
Approximation Technique ◽  
Contamination Level ◽  
Spatial Filters ◽  
Frequency Bands ◽  
Computer Interfaces ◽  
The Difference

Electroencephalography (EEG)-based brain computer interfaces (BCIs) translate motor imagery commands into the movements of an external device (e.g., a robotic arm). The automatic design of spectral and spatial filters is a challenging task, as the frequency bands of the spectral filters must be predefined by previously published studies and given that they may be affected during trials by artifacts and improper motor imagery (MI). This study aimed to eliminate the contaminated trials automatically during classifier training, and to simultaneously learn the spectral and spatial patterns without the need for predefined frequency bands. Compared with previous studies that measured the discriminative power of a frequency band based on mutual information, this study determined the difference of the class conditional probability density function between two MI classes. This information was further shared to measure the contamination level of the trial that simplified the computation. A particle-based approximation technique iteratively constructed a filter bank that extracted discriminative features, and simultaneously removed potentially contaminated trials. The particle weight was estimated by an analysis of variance F-test instead of mutual information as commonly used in previous studies. The experimental results of a publicly available dataset revealed that the proposed method outperformed the other BCI in terms of the classification accuracy. Asymmetrical spatial patterns were found on left- versus right-hand MI classifications. The learnt spectral and spatial patterns were consistent with prior neurophysiological knowledge.

scholarly journals “Mine Works Better”: Examining the Influence of Embodiment in Virtual Reality on the Sense of Agency During a Binary Motor Imagery Task With a Brain-Computer Interface

2021 ◽  
Vol 12 ◽  
Author(s):  
Hamzah Ziadeh ◽  
David Gulyas ◽  
Louise Dørr Nielsen ◽  
Steffen Lehmann ◽  
Thomas Bendix Nielsen ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Poor Performance ◽  
Structured Interview ◽  
Sense Of Agency ◽  
Imagery Task ◽  
Future Studies ◽  
Computer Interfaces ◽  
Healthy Participants

Motor imagery-based brain-computer interfaces (MI-BCI) have been proposed as a means for stroke rehabilitation, which combined with virtual reality allows for introducing game-based interactions into rehabilitation. However, the control of the MI-BCI may be difficult to obtain and users may face poor performance which frustrates them and potentially affects their motivation to use the technology. Decreases in motivation could be reduced by increasing the users' sense of agency over the system. The aim of this study was to understand whether embodiment (ownership) of a hand depicted in virtual reality can enhance the sense of agency to reduce frustration in an MI-BCI task. Twenty-two healthy participants participated in a within-subject study where their sense of agency was compared in two different embodiment experiences: 1) avatar hand (with body), or 2) abstract blocks. Both representations closed with a similar motion for spatial congruency and popped a balloon as a result. The hand/blocks were controlled through an online MI-BCI. Each condition consisted of 30 trials of MI-activation of the avatar hand/blocks. After each condition a questionnaire probed the participants' sense of agency, ownership, and frustration. Afterwards, a semi-structured interview was performed where the participants elaborated on their ratings. Both conditions supported similar levels of MI-BCI performance. A significant correlation between ownership and agency was observed (r = 0.47, p = 0.001). As intended, the avatar hand yielded much higher ownership than the blocks. When controlling for performance, ownership increased sense of agency. In conclusion, designers of BCI-based rehabilitation applications can draw on anthropomorphic avatars for the visual mapping of the trained limb to improve ownership. While not While not reducing frustration ownership can improve perceived agency given sufficient BCI performance. In future studies the findings should be validated in stroke patients since they may perceive agency and ownership differently than able-bodied users.

The Science of Wisdom in a Polarized World: Knowns and Unknowns

2020 ◽  
Author(s):  
Igor Grossmann ◽  
Nic M. Weststrate ◽  
Monika Ardelt ◽  
Justin Peter Brienza ◽  
Mengxi Dong ◽  
...  
Keyword(s):  
Individual Differences ◽  
Problem Solving ◽  
Best Practices ◽  
Cognitive Sciences ◽  
Methodological Challenges ◽  
The Common ◽  
Empirical Approaches ◽  
And Training ◽  
Contextual Differences

Interest in wisdom in the cognitive sciences, psychology, and education has been paralleled by conceptual confusions about its nature and assessment. To clarify these issues and promote consensus in the field, wisdom researchers met in Toronto in July of 2019, resolving disputes through discussion. Guided by a survey of scientists who study wisdom-related constructs, we established a common wisdom model, observing that empirical approaches to wisdom converge on the morally-grounded application of metacognition to reasoning and problem-solving. After outlining the function of relevant metacognitive and moral processes, we critically evaluate existing empirical approaches to measurement and offer recommendations for best practices. In the subsequent sections, we use the common wisdom model to selectively review evidence about the role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, as well as cultural, subcultural, and social-contextual differences. We conclude by discussing wisdom’s conceptual overlap with a host of other constructs and outline unresolved conceptual and methodological challenges.

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

Sign in / Sign up

Export Citation Format

Share Document