Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification

2015 ◽  
Vol 25 (04) ◽  
pp. 1550013 ◽  
Author(s):  
Ye Liu ◽  
Qibin Zhao ◽  
Liqing Zhang
Keyword(s):  
Motor Imagery ◽  
Activity Patterns ◽  
Individual Variability ◽  
Superior Performance ◽  
Eeg Classification ◽  
Frequency Bands ◽  
Practical Applications ◽  
Temporal Domain ◽  
Discriminative Subspace ◽  
Eeg Data

Motor imagery-based brain–computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usually loses effects due to individual variability among different subjects in practical applications. In this study, a robust tensor-based method is proposed for a multiway discriminative subspace extraction from tensor-represented EEG data, which performs well in motor imagery EEG classification without the prior neurophysiologic knowledge like channels configuration and active frequency bands. Motor imagery EEG patterns in spatial-spectral-temporal domain are detected directly from the multidimensional EEG, which may provide insights to the underlying cortical activity patterns. Extensive experiment comparisons have been performed on a benchmark dataset from the famous BCI competition III as well as self-acquired data from healthy subjects and stroke patients. The experimental results demonstrate the superior performance of the proposed method over the contemporary methods.

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 Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

2021 ◽  
Vol 9 (2) ◽  
pp. 541-553
Author(s):  
Rameshwar D. Chintamani, Et. al.
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Feature Extraction ◽  
Motor Imagery ◽  
Human Body ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Eeg Classification ◽  
Eeg Data

The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity. 

Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification

2016 ◽  
Vol 27 (02) ◽  
pp. 1650032 ◽  
Author(s):  
Yu Zhang ◽  
Yu Wang ◽  
Jing Jin ◽  
Xingyu Wang
Keyword(s):  
Motor Imagery ◽  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Frequency Bands ◽  
Eeg Data ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
Selection Of

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.

In situ TEM Studies of agglomeration of sub-nanometer Ru layers in Ru/C multilayers

1992 ◽  
Vol 50 (1) ◽  
pp. 256-257
Author(s):  
Tai D. Nguyen ◽  
Ronald Gronsky ◽  
Jeffrey B. Kortright
Keyword(s):  
Layered Structure ◽  
Driving Force ◽  
High Energy ◽  
Superior Performance ◽  
In Situ Tem ◽  
Rayleigh Instability ◽  
Practical Applications ◽  
Transmission Electron ◽  
Diffraction Patterns

Nanometer period Ru/C multilayers are one of the prime candidates for normal incident reflecting mirrors at wavelengths < 10 nm. Superior performance, which requires uniform layers and smooth interfaces, and high stability of the layered structure under thermal loadings are some of the demands in practical applications. Previous studies however show that the Ru layers in the 2 nm period Ru/C multilayer agglomerate upon moderate annealing, and the layered structure is no longer retained. This agglomeration and crystallization of the Ru layers upon annealing to form almost spherical crystallites is a result of the reduction of surface or interfacial energy from die amorphous high energy non-equilibrium state of the as-prepared sample dirough diffusive arrangements of the atoms. Proposed models for mechanism of thin film agglomeration include one analogous to Rayleigh instability, and grain boundary grooving in polycrystalline films. These models however are not necessarily appropriate to explain for the agglomeration in the sub-nanometer amorphous Ru layers in Ru/C multilayers. The Ru-C phase diagram shows a wide miscible gap, which indicates the preference of phase separation between these two materials and provides an additional driving force for agglomeration. In this paper, we study the evolution of the microstructures and layered structure via in-situ Transmission Electron Microscopy (TEM), and attempt to determine the order of occurence of agglomeration and crystallization in the Ru layers by observing the diffraction patterns.

scholarly journals Consolidated bioprocessing of lignocellulose for production of glucaric acid by an artificial microbial consortium

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chaofeng Li ◽  
Xiaofeng Lin ◽  
Xing Ling ◽  
Shuo Li ◽  
Hao Fang
Keyword(s):  
Acid Production ◽  
Microbial Consortium ◽  
Consolidated Bioprocessing ◽  
High Titer ◽  
Value Added ◽  
Superior Performance ◽  
Glucaric Acid ◽  
Practical Applications ◽  
Work Distribution ◽  
Rut C30

Abstract Background The biomanufacturing of d-glucaric acid has attracted increasing interest because it is one of the top value-added chemicals produced from biomass. Saccharomyces cerevisiae is regarded as an excellent host for d-glucaric acid production. Results The opi1 gene was knocked out because of its negative regulation on myo-inositol synthesis, which is the limiting step of d-glucaric acid production by S. cerevisiae. We then constructed the biosynthesis pathway of d-glucaric acid in S. cerevisiae INVSc1 opi1Δ and obtained two engineered strains, LGA-1 and LGA-C, producing record-breaking titers of d-glucaric acid: 9.53 ± 0.46 g/L and 11.21 ± 0.63 g/L d-glucaric acid from 30 g/L glucose and 10.8 g/L myo-inositol in fed-batch fermentation mode, respectively. However, LGA-1 was preferable because of its genetic stability and its superior performance in practical applications. There have been no reports on d-glucaric acid production from lignocellulose. Therefore, the biorefinery processes, including separated hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF) and consolidated bioprocessing (CBP) were investigated and compared. CBP using an artificial microbial consortium composed of Trichoderma reesei (T. reesei) Rut-C30 and S. cerevisiae LGA-1 was found to have relatively high d-glucaric acid titers and yields after 7 d of fermentation, 0.54 ± 0.12 g/L d-glucaric acid from 15 g/L Avicel and 0.45 ± 0.06 g/L d-glucaric acid from 15 g/L steam-exploded corn stover (SECS), respectively. In an attempt to design the microbial consortium for more efficient CBP, the team consisting of T. reesei Rut-C30 and S. cerevisiae LGA-1 was found to be the best, with excellent work distribution and collaboration. Conclusions Two engineered S. cerevisiae strains, LGA-1 and LGA-C, with high titers of d-glucaric acid were obtained. This indicated that S. cerevisiae INVSc1 is an excellent host for d-glucaric acid production. Lignocellulose is a preferable substrate over myo-inositol. SHF, SSF, and CBP were studied, and CBP using an artificial microbial consortium of T. reesei Rut-C30 and S. cerevisiae LGA-1 was found to be promising because of its relatively high titer and yield. T. reesei Rut-C30 and S. cerevisiae LGA-1were proven to be the best teammates for CBP. Further work should be done to improve the efficiency of this microbial consortium for d-glucaric acid production from lignocellulose.

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 Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Yunfa Fu ◽  
Jian Chen ◽  
Xin Xiong
Keyword(s):  
Grip Force ◽  
Activity Patterns ◽  
Hand Movement ◽  
Local Maximum ◽  
Topographic Map ◽  
Mu Rhythm ◽  
Global Field Power ◽  
Frequency Bands ◽  
Microstate Analysis ◽  
Imagined Movement

Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.

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