scholarly journals Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1387 ◽  
Author(s):  
Korovesis ◽  
Kandris ◽  
Koulouras ◽  
Alexandridis
Keyword(s):  
Motion Control ◽  
Brain Computer Interface ◽  
Neuromuscular Disorders ◽  
Research Work ◽  
Experimental Tests ◽  
Computer Interface ◽  
Human Machine Interaction ◽  
Gender And Age ◽  
Robotic Vehicle ◽  
Machine Interaction

Modern achievements accomplished in both cognitive neuroscience and human–machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain–Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes’ blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain–Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.

scholarly journals A Comprehensive Review on Brain-Computer Interface Controlled Movements

2019 ◽  
Vol 5 (6) ◽  
pp. 3
Author(s):  
Kulsheet Kaur Virdi ◽  
Satish Pawar
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Neuromuscular Disorders ◽  
Research Work ◽  
Brain Structures ◽  
Computer Interface ◽  
Body Parts ◽  
Machine Interface ◽  
The Brain

A brain-computer interface (BCI), also referred to as a mind-machine interface (MMI) or a brain-machine interface (BMI), provides a non-muscular channel of communication between the human brain and a computer system. With the advancements in low-cost electronics and computer interface equipment, as well as the need to serve people suffering from disabilities of neuromuscular disorders, a new field of research has emerged by understanding different functions of the brain. The electroencephalogram (EEG) is an electrical activity generated by brain structures and recorded from the scalp surface through electrodes. Researchers primarily rely on EEG to characterize the brain activity, because it can be recorded noninvasively by using portable equipment. The EEG or the brain activity can be used in real time to control external devices via a complete BCI system. For these applications there is need of such machine learning application which can be efficiently applied on these EEG signals. The aim of this research is review different research work in the field of brain computer interface related to body parts movements.

scholarly journals Artifact Removal and of EEG Signal Classification for Brain Computer Interface (BCI) using Back Propagation

2020 ◽  
Vol 8 (6) ◽  
pp. 1275-1282
Keyword(s):  
Brain Computer Interface ◽  
Research Work ◽  
Back Propagation ◽  
Software Tool ◽  
Computer Interface ◽  
Eeg Signals ◽  
Biological Sources ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
The Brain

A brain-computer interface (BCI) provides a communication passage between the brain and an external stratagem. The Brain and its EEG signals are acquired from the BCI along its control signals and its widely used mechanism in the field of the biomedical fields. In this research work, an artifacts are removed algorithm in the EEG is developed and simulated in the MATLAB 2017a software tool. EEG signals from patients are recoded while recording some of the artificial signals added to it, which are instigated by using eye blinks, eye movement, muscle, and cardiac noise, and also non-biological sources. Using suitable filters these artificial signals can be removed. This paper aims to remove the artificial signals from EEG signals and parameters like mean, standard. Deviation are calculated and compared with other methods such as LAMICA and FASTERs. In the paper, it is also the proposed arrangement of EEG signals for the discovery of typical and anomalous exercises utilizing Wavelet change and Artificial Neural Network (ANN) Classifier is considered. Here, the framework utilizes the back proliferation with feed-forward for order which pursues the ANN grouping. Accuracy of the classification is calculated and compared with other states of art publications and found that it is better.

scholarly journals Planeness Measurement of Computer Harddisk Surface Based on Opto-Mechatronics Technology

2013 ◽  
Vol 7 (2) ◽  
pp. 171-175 ◽  
Author(s):  
Rui-Yin Tang ◽  
◽  
Zhou-Mo Zeng ◽  
Hong-Kun He ◽  
Zhi-Kun Chen ◽  
...  
Keyword(s):  
Motion Control ◽  
High Efficiency ◽  
High Accuracy ◽  
Reliable Control ◽  
Human Machine Interaction ◽  
Detailed Design ◽  
Control Module ◽  
Novel Method ◽  
Machine Interaction ◽  
Electric Detection

This paper presents a novel method based on optomechatronics for highly accurately detecting the planeness of computer hard-disk surfaces. This article introduces the detailed design of the system, which includes four modules: the optic-electric detection module, the 2-dimensional mechanic platform, the motion control module and the human-machine interaction module. Results show that the system has merits like non-contact, high accuracy, flexible and reliable control and high efficiency. The system has been applied successfully at a foreign-funded enterprise in Singapore.

scholarly journals EXPERIMENTAL AND THEORETICAL FOUNDATIONS AND PRACTICAL IMPLEMENTATION OF TECHNOLOGY BRAIN-COMPUTER INTERFACE

2013 ◽  
Vol 12 (2) ◽  
pp. 21-29 ◽  
Author(s):  
A. Ya. Kaplan ◽  
A. G. Kochetova ◽  
S. L. Shishkin ◽  
I. A. Basyul ◽  
I. P. Ganin ◽  
...  
Keyword(s):  
Motor Function ◽  
Brain Computer Interface ◽  
Neuromuscular Disorders ◽  
Healthy Person ◽  
Computer Interface ◽  
Practical Implementation ◽  
Critical Fields ◽  
Technological Base ◽  
The Brain ◽  
Theoretical Foundations

Technology brain-computer interface (BCI) allow saperson to learn how to control external devices via thevoluntary regulation of own EEG directly from the brain without the involvement in the process of nerves and muscles. At the beginning the main goal of BCI was to replace or restore motor function to people disabled by neuromuscular disorders. Currently, the task of designing the BCI increased significantly, more capturing different aspects of life a healthy person. This article discusses the theoretical, experimental and technological base of BCI development and systematized critical fields of real implementation of these technologies.

scholarly journals Improving the Performance of Brain-Computer Interface using Deep Learning Algorithms and Event-Related Spectral Perturbation

2020 ◽  
Vol 8 (6) ◽  
pp. 3756-3763
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Computer Interface ◽  
Neuromuscular Disorders ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Computer Interface ◽  
Brain Signals ◽  
Spectral Perturbation

Brain Computer Interface allows disabled people to communicate with the external world by using their brain signals. The main goal of a BCI is to provide patients who suffer form any neuromuscular disorders whith a communication channel based on their brain signals. In this paper, the aim is to explore the effects of applying deep learning algorithms and Event Related Spectral Perturbation analyses on the performance of different EEG-based BCI paradigms. Two paradigms were investigated: one is based on the Matrix paradigm (known as oddball); and the other one utilizes the Rapid serial visual Presentation (RSVP) for presenting the stimuli. Deep learning algorithms of convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were utilized to evaluate the two paradigms. Our findings showed that Matrix paradigm is more effective in detecting P300 signal. In terms of classification methods, deep learning of CNN algorithm has shown superiority performance in comparison with the other machine learning algorithms.

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