Online Brain Computer Interface Based Five Classes EEG To Control Humanoid Robotic Hand

Faiz, Mohammed Zeki Al; Al-Hamadani, Ammar A.

doi:10.1109/tsp.2019.8769072

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Likewise, Barachant et al [ 176 ] presented a new classification method based on Riemannian geometry that uses covariance matrices to classify multi-class BCI. Faiz and Hamadani [ 177 ] controlled humonoid robotic hand gentures through five class online MI BCI while using a commercial EEG headset. They user AR and CSP feature extractions and PCA to reduce the dimension of AR features.…”

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

103

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Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

103

View full text Add to dashboard Cite

show abstract

“…EEG studies on control; Karaduman and Karcı (Karaduman and Karcı 2020), recorded EEG signals by adapting on the basis of eye and arm movements and suggested that the devices could be controlled by ensuring the correlation of detection of these movements. Faiz and Al-Hamadani (Faiz and Al-Hamadani 2019), extracted attributes from EEG signals using Autoregressive (AR) coefficients and Common Spatial Pattern (CSP) methods and ensured the control of the humanoid robot hand by classifying them. They ensured the control of the robot arm by obtaining an accuracy rate of 97.5% from the results they divided into 5 classes.…”

Section: Related Workmentioning

confidence: 99%

Determining the Demands of Disabled People by Artificial Intelligence Methods

Karaduman¹,

Karcı²

2021

Computer Science

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Analysis of brain activities and remote control are among the current issues that are being studied. Analysis of signals arising during brain functions is electroencephalography (EEG). EEG signals have intellectual, visual stimulation, and motion resultant forms. Especially, EEG signals generated by visual stimulus are within the scope of this study. In this study, research was carried out on the classification of EEG signals formed in a person looking at visual figures. For these studies, first of all, EEG signals from the brain were recorded with images and filtered to remove noise. Then, the features were extracted from the signals. In this study, Moment 5 feature was also used in addition to the features used in many studies such as mean, median, standard deviation and entropy. Then, classification was made using Support Vector Machine (SVM), k Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. Classification was made for 4 different visual shapes used, since these shapes are square, circle, triangle, and star, and the same categorical names were used in the classification stage. As a result of the classification of EEG signals; SVM and KNN algorithms have determined which shape is viewed with 99.99% accuracy. These results show that different signals are produced in the brain according to the structure of the shape viewed. This situation shows that it can be used as a method to give patients the opportunity to express their requests just by looking or thinking.

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“…Deep learning (DL) is a paradigm within ML based on the use of artificial neural networks (ANNs) [41]. Commonly, ML algorithms focus on classifying EEG signals related to the motor and imaginary movements of hands and feet to carry out control actions, as presented in [43][44][45][46]. DL is useful in areas with vast and high-dimensional data; therefore, deep neural networks outperform ML algorithms for most text, images, video, voice, and audio processing techniques [47].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

et al. 2022

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In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

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Online Brain Computer Interface Based Five Classes EEG To Control Humanoid Robotic Hand

Cited by 9 publications

References 8 publications

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Determining the Demands of Disabled People by Artificial Intelligence Methods

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

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