Prototype design for bidirectional control of stepper motor using features of brain signals and soft computing tools

2022 ◽  
Vol 71 ◽  
pp. 103245
Author(s):  
Gauri Shanker Gupta ◽  
Prabhat Ranjan Tripathi ◽  
Shikhar Kumar ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Soft Computing ◽  
Stepper Motor ◽  
Brain Signals ◽  
Prototype Design

DESIGN OF CONTROL SYSTEM FOR MOTOR IMAGERY BASED NEURO-AID APPLICATION

2019 ◽  
Vol 31 (04) ◽  
pp. 1950031
Author(s):  
Gauri Shanker Gupta ◽  
Maanvi Bhatnagar ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Closed Loop ◽  
Open Loop ◽  
Stepper Motor ◽  
Support Vector ◽  
Loop Analysis ◽  
Training Time ◽  
Brain Signals ◽  
And Performance ◽  
Learning Machine

The application of Brain Computer Interface (BCI) for rehabilitation purpose has gained wide popularity in recent times. BCI for rehabilitation involves detection of brain signals, when the subject performs some sort of Motor Imagery (MI) task, for example, imagination of movement of limbs. Imagination of such movement causes desynchronization of neurons of one part of the brain gets within other parts synchronized. Band power features are best suited for quantification of the synchronization phenomenon. In the present work, extreme learning machine (ELM) and support vector machine (SVM) based classifiers are used to classify the test data. The classifier output is further used to generate control signals for driving a stepper motor, which may be used to drive some neuro-aid application device. In order to achieve a workable model for pragmatic applications, it is necessary to design a robust in nature stepper motor. Open loop analysis, closed loop analysis and performance analysis of motor with possible disturbances are carried out to evaluate the effectiveness of the proposed work. The maximum accuracy using ELM and SVM classifiers are achieved as 90% and 87.78% with a training time of 0.2496[Formula: see text]s and 3.964[Formula: see text]s, respectively. In the open loop and closed loop analysis, the desired angular movement (task imagined for rehabilitation) is achieved with an accuracy of 54.14% and 93.4%, respectively. These results suggest that a BCI system can be designed with higher efficiency with the help of MI data.

Soft Computing in Electromagnetics

2015 ◽  
Author(s):  
Balamati Choudhury ◽  
Rakesh Mohan Jha
Keyword(s):  
Soft Computing

INVESTIGATION OF POSITION ELECTRIC DRIVE BASED ON STEPPER MOTOR IN MICROSTEPPING MODE

2015 ◽  
Vol 19 (95) ◽  
pp. 24-27
Author(s):  
Elena S. Nazarova ◽  
◽  
Vladimir V. Osadchii ◽  
Sergej Ju. Tobolkin ◽  
◽  
...  
Keyword(s):  
Electric Drive ◽  
Stepper Motor

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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