Elucidation of Visual Motor Imagery EEG Data Using Feature Streamlining with Machine Learning | Chapter 8 | Mathematics and Computer Science: Contemporary Developments Vol. 9

Visual motor imagery (VMI) is a cognitive process where individuals visualize performing a motor task without actual movement. Electroencephalography (EEG) data is often used to analyze brain activity associated with VMI. However, the high dimensionality and complexity of EEG data pose challenges for effective analysis. This study focuses on streamlining EEG features using advanced machine-learning techniques to enhance the accuracy and efficiency of VMI classification. We applied feature selection and extraction methods to identify the most relevant features from EEG signals, followed by the implementation of various machine learning algorithms to classify VMI tasks. Our results demonstrate that feature streamlining significantly reduces computational load while maintaining high classification accuracy. The streamlined features, combined with optimized machine learning models, provide a robust framework for interpreting VMI-related EEG data, paving the way for improved brain-computer interface (BCI) systems. This approach has potential applications in neurorehabilitation, cognitive training, and enhancing user interaction in BCI technologies. Classifying motor imagery in the EEG is crucial for identifying serious illnesses. The diversity of the motor imagery EEG data hampered the rate of categorization. The motor imagery EEG classification rate is increased using the feature optimization procedure. A deep neural network-based classifier for motor imagery EEG categorization was suggested in this article. The design deep neural network is a three-layer neural network model that incorporates the teacher learning-based optimization and feature optimization technique. The EEG data's noise and artefacts are reduced using a teacher learning-based optimization technique, which also enhances the input vectors for DNN. The suggested approach has been tested on datasets from the third and fourth BCI competitions and has been simulated in MATLAB settings. The evaluation's findings indicate that the suggested technique is quite effective at compressing the current motor.

 

Author (s) Details

Virendra Kumar Tiwari
Lakshmi Narain College of Technology (MCA), Bhopal, India.

 

Priyanka Singh
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Vinita Keer
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Gaurav Kishor Saxena
Lakshmi Narain College of Technology (MCA), Bhopal, India.

 

Sandeep Dubey
Lakshmi Narain College of Technology Excellence, Bhopal, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v9/3416

Exploring the Potential of EEG Signals in Mental State Analysis | Chapter 1 | Medical Research and Its Applications Vol. 3

 Electroencephalogram (EEG) is a method known as electrophysiology for monitoring and recording the electrical signal activity of the brain. Electroencephalogram (EEG) signals from the human brain have a ground-breaking impact in the field of healthcare industry. EEG is one of the most actively used biosignals as a measurement tool in the Brain-Computer Interface (BCI) to implement “thoughts or intentions” based on human and machine interaction. In the medical field, EEG-based interaction is also used for computer-aided diagnosis for patients and also to help in detecting emotions and body movements. In this paper, the beneficial aspect of EEG is demonstrated which can be a great help for paralysed patients. In this study, especially the consciousness of human beings through analysis of the values of EEG to predict the physical condition of that particular human is observed. The study of characteristics of the signals while the person is moving and when the person intends to move to be diagnosed through BCI can be controlled for humanoid movement through EEG.

 

Author(s)details:-

 

Susmita Das
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Trisha Paul
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Chaitali Bhattacharyya
Electronics and Instrumentation Engineering, Narula Institute of Technology, India.

 

Please See the book here :-https://doi.org/10.9734/bpi/mria/v3/3472G

Electroencephalography-Based Inner Speech Classification Using LSTM and Wavelet Scattering Transformation (WST) | Chapter 3 | Contemporary Perspective on Science, Technology and Research Vol. 3

In this place paper, we propose an imagined talk-based idea pattern recognition using deep education. Multiple features were culled concurrently from eight-channel Electroencephalography (EEG) signals. Imagined talk, sometimes named inner speech, is an wonderful choice for decoding human thinking using the Mind-Computer Interface (BCI) idea. BCI is being developed to happening slowly allow paralyzed victims to interact directly accompanying their environment. To acquire classifiable EEG data with lean number of sensors, we placed the EEG sensors on carefully picked spots on the scalp. To decrease the dimensions and complicatedness of the EEG dataset and to avoid overfitting all along the deep learning invention, we utilized the wavelet scattering renewal. The study was conducted in the Department of Energetic & Computer Metallurgy and Computer Science at Jackson State University, United states of america. A low-cost 8-channel EEG headset was secondhand with MATLAB 2023a to get the EEG data. The Long-Short Term Thought Recurrent Neural Network (LSTM-RNN) was used to decipher the identified EEG signals into four audio commands: Up, Below, Left, and Right. Wavelet uneven transformation was applied to extract ultimate stable features avoid the EEG dataset through a series of filtration processes. Filtration has been executed for each individual command in the EEG datasets. The projected imagined speech-located brain wave pattern recognition approach obtained a 92.50% overall classification veracity. This accuracy is promising for plotting a trustworthy imagined talk-based Brain-Calculating Interface (BCI) future authentic-time systems. For better judgment of the classification performance, additional metrics were considered, and we got 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, individually. Future work is planned to implement and test an online BCI order using MATLAB/Simulink and G. tec Unicorn Mixture Black+ bias.

Author(s) Details:

Mokhles M. Abdulghani,
Jackson State University, MS, USA.

Wilbur L. Walters,
Jackson State University, MS, USA.

Khalid H. Abed,
Jackson State University, MS, USA.

Please see the link here: https://stm.bookpi.org/CPSTR-V3/article/view/12915 

Consumer BCI Devices - Applications and Challenges | Chapter 8 | Advanced Aspects of Engineering Research Vol. 11

 The Brain-Computer-Interface (BCI) is a system that collects, analyses, and converts brain signals into commands that can assist humans in controlling their surroundings. BCI is employed in a variety of applications, including security, medical, games, self-regulation, and research. Using various Electroencephalography (EEG) devices, BCI provides a mutual link between the brain and the outside environment. Many people are paralysed today, and these EEG devices can assist them in communicating with the outside world. This chapter will look at different EEG devices with varying numbers of electrodes, their benefits and drawbacks, and applications that will benefit society in the long term.

Author (s) Details

Veena N.
Information Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

S. Mahalakshmi
Information Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

Guruprasad S.
Computer Science and Engineering, BMS Institute of Technology & Management (BMSIT&M), Bengaluru, India.

View Book : https://stm.bookpi.org/AAER-V11/article/view/1253