Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification

The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy; 14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.

Analyzing EEG patterns in young adults exposed to different acrophobia levels: a VR study

Introduction

The primary objective of this research is to examine acrophobia, a widely prevalent and highly severe phobia characterized by an overwhelming dread of heights, which has a substantial impact on a significant proportion of individuals worldwide. The objective of our study was to develop a real-time and precise instrument for evaluating levels of acrophobia by utilizing electroencephalogram (EEG) signals.

Methods

EEG data was gathered from a sample of 18 individuals diagnosed with acrophobia. Subsequently, a range of classifiers, namely Support Vector Classifier (SVC), K-nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Adaboost, Linear Discriminant Analysis (LDA), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), were employed in the analysis. These methodologies encompass both machine learning (ML) and deep learning (DL) techniques.

Results

The Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) models demonstrated notable efficacy. The Convolutional Neural Network (CNN) model demonstrated a training accuracy of 96% and a testing accuracy of 99%, whereas the Artificial Neural Network (ANN) model attained a training accuracy of 96% and a testing accuracy of 97%. The findings of this study highlight the effectiveness of the proposed methodology in accurately categorizing real-time degrees of acrophobia using EEG data. Further investigation using correlation matrices for each level of acrophobia showed substantial EEG frequency band connections. Beta and Gamma mean values correlated strongly, suggesting cognitive arousal and acrophobic involvement could synchronize activity. Beta and Gamma activity correlated strongly with acrophobia, especially at higher levels.

Discussion

The results underscore the promise of this innovative approach as a dependable and sophisticated method for evaluating acrophobia. This methodology has the potential to make a substantial contribution toward the comprehension and assessment of acrophobia, hence facilitating the development of more individualized and efficacious therapeutic interventions.

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal

Introduction

The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks.

Methods

TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification.

Results

Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy.

Conclusion

The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals.

Highlights

Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task.

Plain Language Summary

In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.

Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial

Introduction

Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB.

Methods

Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and μ-event-related desynchronization (ERD) and vividness MI were evaluated.

Results

μ-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training.

Discussion

Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.

Age-dependent Electroencephalogram Characteristics During Different Levels of Anesthetic Depth

Objective The monitoring of anesthetic depth based on electroencephalogram derivation is not currently adjusted for age. Here we analyze the influence of age factors on electroencephalogram characteristics. Methods Frontal electroencephalogram recordings were obtained from 80 adults during routine clinical anesthesia. The characteristics of electroencephalogram with age and anesthesia were observed during four kinds of anesthesia. Results The slow wave power, δ power, Bispectral Index (BIS) and approximate entropy can be used to distinguish different states of anesthesia (P < 0.05). In the deep and very deep anesthesia states, δ power decreased with age (P < 0.0001). In the very deep anesthesia state, θ power decreased with age (P < 0.05). In the deep and very deep anesthesia states, α power decreased with age (P = 0.0002). In the light and deep anesthesia states, β power decreased with age (P = 0.003). In the deep anesthesia state, γ power decreased with age (P = 0.002). In the very deep anesthesia state, permutation entropy increased significantly with age (P = 0.0001). In the very deep anesthesia state, BIS value increased with age (P = 0.006). The slow wave power, approximate entropy, and sample entropy did not show age-dependent changes. Conclusions The influence of age should be considered when using BIS and δ power to monitor the depth of anesthesia, while the influence of age should not be considered when using slow wave power and approximate entropy to monitor the depth of anesthesia.