EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

2022 ◽  
Author(s):  
Yuan Liu ◽  
Zhuang Wang ◽  
Shuaifei Huang ◽  
Wenjie Wang ◽  
Dong Ming
Keyword(s):  
Frontal Lobe ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Motor Area ◽  
Support Vector ◽  
Human Hand ◽  
New Paradigm ◽  
Time Frequency ◽  
Spatial Features ◽  
Robotic Devices

Abstract Objective. Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI). Approach. 14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm. Main results. ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01). Significance. This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

scholarly journals Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Baolei Xu ◽  
Yunfa Fu ◽  
Gang Shi ◽  
Xuxian Yin ◽  
Zhidong Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Selection Criterion ◽  
Support Vector ◽  
Classification Rate ◽  
Time Frequency ◽  
Significant Difference ◽  
Motor Parameter ◽  
Frequency Phase

We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3496
Author(s):  
Jiacan Xu ◽  
Hao Zheng ◽  
Jianhui Wang ◽  
Donglin Li ◽  
Xiaoke Fang
Keyword(s):  
Frequency Domain ◽  
Motor Imagery ◽  
Recognition Performance ◽  
Feature Learning ◽  
Feature Representation ◽  
Support Vector ◽  
Imagery Task ◽  
Learning Method ◽  
Eeg Signals ◽  
Time Frequency

Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

A Comparative Study of Linear and Non-Linear Classifiers in Sensory Motor Imagery Based Brain Computer Interface

2019 ◽  
Vol 16 (12) ◽  
pp. 5134-5139 ◽  
Author(s):  
Poonam Chaudhary ◽  
Rashmi Agrawal
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Neural Signals ◽  
Linear Classifiers ◽  
Sensory Motor ◽  
Non Linear

Over the period of 4–5 decades of the field electrophysiology on neural signal events in single cell recording, it can be concluded that there is production of far field potentials containing near synchronous field patterns might reach at the scalp electrodes. These neural signals then can be analyzed and converted into the control signals for computers and other electronic devices. Electroencephalography (EEG) is a method to acquire these neural signals from the scalp of human brain. EEG signals are simple, economical and have high temporal resolution properties. These properties make it advantageous to use widely in the medical as well as non-medical applications. The event related synchronization and desynchronization (ERS/ERD) pattern present in EEG during sensory motor imagery (SMI) process over the cortical area is an important feature to take BCI towards realistic approach. The accurate classification of these ERS/ERD pattern present in EEG signal is dependent on classification accuracy of different classifiers. So, the objective of the paper is to analyze the classification accuracy of linear and non-linear classifiers used for BCI system design. This paper also presents the comparative study of linear (Linear discriminant analysis and Support Vector Machine) and non-linear classifiers (Bayesian and Radial Basis Function-Support Vector Machine) using BCI competition IV dataset 2a. The result concluded that linear classifiers (LDA and SVM) have outperformed the non-linear classifiers on EEG data with the average performance across subjects 75.26±12.23, 72.42±11.12.

scholarly journals COMPARISONS BETWEEN MOTOR AREA EEG AND ALL-CHANNELS EEG FOR TWO ALGORITHMS IN MOTOR IMAGERY TASK CLASSIFICATION

2014 ◽  
Vol 26 (03) ◽  
pp. 1450040 ◽  
Author(s):  
Siuly ◽  
Yan Li ◽  
Peng Wen
Keyword(s):  
Motor Imagery ◽  
Binary Logistic Regression ◽  
Classification Performance ◽  
Least Square ◽  
Motor Area ◽  
Support Vector ◽  
Imagery Task ◽  
Task Classification ◽  
Svm Algorithm ◽  
The Brain

This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.

scholarly journals Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Chu Kiong Loo ◽  
Andrews Samraj ◽  
Gin Chong Lee
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Estimation Methods ◽  
Support Vector ◽  
Linear Discriminant

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

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 Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Elena N. Pitsik ◽  
Vyacheslav Yu. Musatov ◽  
Anastasia E. Runnova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Imagery ◽  
High Frequency ◽  
Classification Accuracy ◽  
Eeg Signals ◽  
Pass Filter ◽  
Time Frequency ◽  
Artificial Neural

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

scholarly journals Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

2021 ◽  
Vol 38 (1) ◽  
pp. 13-26
Author(s):  
Hesam Akbari ◽  
Muhammad Tariq Sadiq ◽  
Malih Payan ◽  
Somayeh Saraf Esmaili ◽  
Hourieh Baghri ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Binary Particle Swarm Optimization ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Central Tendency Measure ◽  
Time Frequency ◽  
Geometrical Features

Late detection of depression is having detrimental consequences including suicide thus there is a serious need for an accurate computer-aided system for early diagnosis of depression. In this research, we suggested a novel strategy for the diagnosis of depression based on several geometric features derived from the Electroencephalography (EEG) signal shape of the second-order differential plot (SODP). First, various geometrical features of normal and depression EEG signals were derived from SODP including standard descriptors, a summation of the angles between consecutive vectors, a summation of distances to coordinate, a summation of the triangle area using three successive points, a summation of the shortest distance from each point relative to the 45-degree line, a summation of the centroids to centroid distance of successive triangles, central tendency measure and summation of successive vector lengths. Second, Binary Particle Swarm Optimization was utilized for the selection of suitable features. At last, the features were fed to support vector machine and k-nearest neighbor (KNN) classifiers for the identification of normal and depressed signals. The performance of the proposed framework was evaluated by the recorded bipolar EEG signals from 22 normal and 22 depressed subjects. The results provide an average classification accuracy of 98.79% with the KNN classifier using city-block distance in a ten-fold cross-validation strategy. The proposed system is accurate and can be used for the early diagnosis of depression. We showed that the proposed geometrical features are better than extracted features in the time, frequency, time-frequency domains as it helps in visual inspection and provide up to 17.56% improvement in classification accuracy in contrast to those features.

Introduction to Motor Imagery-Based Brain-Computer Interface

2020 ◽  
pp. 168-197
Author(s):  
Nitesh Singh Malan ◽  
Shiru Sharma
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Extraction Methods ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Complex Wavelet Transform ◽  
Feature Extraction Method ◽  
Time Frequency

In this chapter, motor imagery (MI) based brain-computer interface (BCI) is introduced incorporating the explanation of key components required to design a practical BCI device. Its application to the medical and nonmedical sector is discussed in detail. In the experimental study, a feature extraction method using time, frequency, and phase analysis of Motor imagery EEG is presented. For the classification of MI task, EEG signals are decomposed using a dual-tree complex wavelet transform (DTCWT) and then time, frequency, and phase features are extracted. The validation of the proposed method is conducted using BCI competition IV dataset 2b. A Support vector machine (SVM) classifier is used to perform the classification task. Performance of the proposed method is compared with the standard feature extraction methods. The proposed scheme achieved a larger average classification accuracy of 82.81% which is better than that obtained by other methods.

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