scholarly journals Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Gu ◽  
Minpeng Xu ◽  
Lichao Xu ◽  
Long Chen ◽  
Yufeng Ke ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Task Allocation ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Instruction Set ◽  
Online Classification ◽  
Eeg Data ◽  
The Common ◽  
The Individual ◽  
Division Of Work

ObjectiveCollaborative brain–computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy’s.ApproachThis study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject’s instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called “2 Tasks” … “5 Tasks.” To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI.Main resultsTaking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at “4 Tasks,” which means each subset contained four instructions. It outperformed the common-work strategy (“6 Tasks”) in classification accuracy (72.29 ± 4.43 vs. 58.53 ± 4.36%).SignificanceThe results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload.

scholarly journals Towards ideal time window for classifying motor imagery in brain-computer interfaces

2020 ◽  
Author(s):  
Vitor Mendes Vilas-Boas ◽  
Vitor Da Silva Jorge ◽  
Cleison Daniel Silva
Keyword(s):  
Motor Imagery ◽  
Time Window ◽  
Brain Activity ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Temporal Properties ◽  
Computer Interfaces ◽  
Motor Intention ◽  
Eeg Data ◽  
The Individual

Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.

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 Finding Top-kCovering Irreducible Contrast Sequence Rules for Disease Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yuhai Zhao ◽  
Yuan Li ◽  
Ying Yin ◽  
Gang Sheng
Keyword(s):  
Classification Accuracy ◽  
Disease Diagnosis ◽  
High Accuracy ◽  
Discriminative Power ◽  
Disease Phenotypes ◽  
The Common ◽  
The Individual ◽  
Sequence Rule ◽  
Insight Into ◽  
Sequence Rules

Diagnostic genes are usually used to distinguish different disease phenotypes. Most existing methods for diagnostic genes finding are based on either the individual or combinatorial discriminative power of gene(s). However, they both ignore the common expression trends among genes. In this paper, we devise a novel sequence rule, namely, top-kirreducible covering contrast sequence rules (TopkIRs for short), which helps to build a sample classifier of high accuracy. Furthermore, we propose an algorithm called MineTopkIRs to efficiently discover TopkIRs. Extensive experiments conducted on synthetic and real datasets show that MineTopkIRs is significantly faster than the previous methods and is of a higher classification accuracy. Additionally, many diagnostic genes discovered provide a new insight into disease diagnosis.

scholarly journals Fuzzy Integral With Particle Swarm Optimization for CNN-Based Motor-Imagery EEG Classification

2021 ◽  
Author(s):  
Jian-Xue Huang ◽  
Chia-Ying Hsieh ◽  
Ya-Lin Huang ◽  
Chun-Shu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Imagery ◽  
Particle Swarm ◽  
Classification Performance ◽  
Fuzzy Integral ◽  
Ensemble Classifiers ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Eeg Data ◽  
Model Training

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed framework aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

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

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Chen ◽  
Wenlong Hang ◽  
Shuang Liang ◽  
Xuejun Liu ◽  
Guanglin Li ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Structural Information ◽  
Classification Performance ◽  
Target Domain ◽  
Alternating Direction ◽  
Learning Procedure ◽  
Eeg Data ◽  
Support Matrix ◽  
Model Training ◽  
Learning Machine

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.

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 A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery

2021 ◽  
Vol 12 (1) ◽  
pp. 482-493
Author(s):  
Zhouzhou Zhou ◽  
Anmin Gong ◽  
Qian Qian ◽  
Lei Su ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Small Sample ◽  
Support Vector ◽  
Hilbert Huang Transform ◽  
Sample Classification ◽  
Eeg Data ◽  
Visual Motor ◽  
Novel Strategy

Abstract A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.

scholarly journals CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

2021 ◽  
Author(s):  
Md Ochiuddin Miah ◽  
Rafsanjani Muhammod ◽  
Khondaker Abdullah Al Mamun ◽  
Dewan Md. Farid ◽  
Shiu Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Motor Imagery ◽  
Classification Performance ◽  
Input Devices ◽  
Ensemble Technique ◽  
Source Codes ◽  
Eeg Data ◽  
Time Motor ◽  
Bio Engineering

Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

scholarly journals Correct block-design experiments mitigate temporal correlation bias in EEG classification

2020 ◽  
Author(s):  
Simone Palazzo ◽  
Concetto Spampinato ◽  
Joseph Schmidt ◽  
Isaak Kavasidis ◽  
Daniela Giordano ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Block Design ◽  
Low Frequency ◽  
Temporal Correlation ◽  
Classification Performance ◽  
Subject Classification ◽  
Main Claim ◽  
Eeg Activity ◽  
Eeg Data

AbstractIt is argued in [1] that [2] was able to classify EEG responses to visual stimuli solely because of the temporal correlation that exists in all EEG data and the use of a block design. While one of the analyses in [1] is correct, i.e., that low-frequency slow EEG activity can inflate classifier performance in block-designed studies [2], as we already discussed in [3], we here show that the main claim in [1] is drastically overstated and their other analyses are seriously flawed by wrong methodological choices. Our counter-analyses clearly demonstrate that the data in [2] show small temporal correlation and that such a correlation minimally contributes to classification accuracy. Thus, [1]’s analysis and criticism of block-design studies does not generalize to our case or, possibly, to other cases. To validate our counter-claims, we evaluate the performance of several state-of-the-art classification methods on the dataset in [2] (after properly filtering the data) reaching about 50% classification accuracy over 40 classes, lower than in [2], but still significant. We then investigate the influence of EEG temporal correlation on classification accuracy by testing the same models in two additional experimental settings: one that replicates [1]’s rapid-design experiment, and another one that examines the data between blocks while subjects are shown a blank screen. In both cases, classification accuracy is at or near chance, in contrast to what [1] reports, indicating a negligible contribution of temporal correlation to classification accuracy. We, instead, are able to replicate the results in [1] only when intentionally contaminating our data by inducing a temporal correlation. This suggests that what Li et al. [1] demonstrate is simply that their data are strongly contaminated by temporal correlation and low signal-to-noise ratio. We argue that the reason why Li et al. in [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience study design recommendations, first and foremost the one of limiting the experiments’ duration, as instead done in [2]. The reduced stimulus-driven neural activity, the removal of breaks and the prolonged duration of experiments in [1], removed the very neural responses that one would hope to classify, leaving only the amplified slow EEG activity consistent with a temporal correlation. Furthermore, the influence of temporal correlation on classification performance in [1] is exacerbated by their choice to perform per-subject classification rather than the more commonly-used and appropriate pooled subject classification as in [2]. Our analyses and reasoning in this paper refute the claims of the “perils and pitfalls of block-design” in [1]. Finally, we conclude the paper by examining a number of other oversimplistic statements, inconsistencies, misinterpretation of machine learning concepts, speculations and misleading claims in [1].NoteThis paper was prepared as a response to [1] before its publication and we were not given access to the code (although its authors had agreed, through the PAMI EiC, to share it with us). For this reason, in the experiments presented in this work we employed our own implementation of their model.

scholarly journals Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

2021 ◽  
Vol 11 (21) ◽  
pp. 10388
Author(s):  
Minh Tran Duc Nguyen ◽  
Nhi Yen Phan Xuan ◽  
Bao Minh Pham ◽  
Trung-Hau Nguyen ◽  
Quang-Linh Huynh ◽  
...  
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Filter Banks ◽  
Classification Performance ◽  
Common Spatial Pattern ◽  
Spatial Filters ◽  
Eeg Data ◽  
Optimal Classification ◽  
Selection Framework ◽  
Fisher’S Ratio

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

Sign in / Sign up

Export Citation Format

Share Document