scholarly journals A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

2021 ◽  
Author(s):  
B Venkata Phanikrishna ◽  
Paweł Pławiak ◽  
Allam Jaya Prakash
Keyword(s):  
Deep Learning ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signal ◽  
Monitoring Method ◽  
Brain Signals ◽  
Processing Techniques

<div>Electro Encephalo Gram (EEG) is a monitoring method used in biomedical and computer science to understand brain activity. Therefore, the analysis and classification of these signals play a prominent role in estimating a person’s behavior to certain events. Manually analyzing these signals is very tedious and time-consuming, so an automated scientific tool is required to analyze the brain signals. In this work, the authors are explored various pre-processing segmentation techniques that are helpful in an automatic machine and deep learning-based classification methods available for EEG signal processing. Most of the machine and deep learning methods are followed pre-processing as a common step in classification. Extraction of the basic sub-band components from EEG signals such as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) is very important in the pre-processing stage. These sub bands of EEG signal have extraordinary evidence related to multiple neurophysiological processes, which are useful for further prediction & diagnosis of diseases and other emotion-based applications. This review paper elaborates various elementary ideas of extracting EEG sub-bands and the role of EEG in Brain-Computer Interface (BCI) in the classification. <b> (Submitted To IEEE reviews in Biomedical Engineering)</b></div>

scholarly journals A Brief Review on EEG Signal Pre-processing Techniques for Real-Time Brain-Computer Interface Applications

2021 ◽  
Author(s):  
B Venkata Phanikrishna ◽  
Paweł Pławiak ◽  
Allam Jaya Prakash
Keyword(s):  
Deep Learning ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signal ◽  
Monitoring Method ◽  
Brain Signals ◽  
Processing Techniques

<div>Electro Encephalo Gram (EEG) is a monitoring method used in biomedical and computer science to understand brain activity. Therefore, the analysis and classification of these signals play a prominent role in estimating a person’s behavior to certain events. Manually analyzing these signals is very tedious and time-consuming, so an automated scientific tool is required to analyze the brain signals. In this work, the authors are explored various pre-processing segmentation techniques that are helpful in an automatic machine and deep learning-based classification methods available for EEG signal processing. Most of the machine and deep learning methods are followed pre-processing as a common step in classification. Extraction of the basic sub-band components from EEG signals such as delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) is very important in the pre-processing stage. These sub bands of EEG signal have extraordinary evidence related to multiple neurophysiological processes, which are useful for further prediction & diagnosis of diseases and other emotion-based applications. This review paper elaborates various elementary ideas of extracting EEG sub-bands and the role of EEG in Brain-Computer Interface (BCI) in the classification. <b> (Submitted To IEEE reviews in Biomedical Engineering)</b></div>

scholarly journals Design of a general brain-computer interface

2011 ◽  
Vol 22 (6) ◽  
pp. 638-646 ◽  
Author(s):  
Alessandro B. Benevides ◽  
Mário Sarcinelli-Filho ◽  
Teodiano F. Bastos Filho
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Eeg Signal ◽  
Time Process ◽  
Class Separation ◽  
Power Spectral ◽  
Mental Tasks

This paper presents the classification of three mental tasks, using the EEG signal and simulating a real-time process, what is known as pseudo-online technique. The Bayesian classifier is used to recognize the mental tasks, the feature extraction uses the Power Spectral Density, and the Sammon map is used to visualize the class separation. The choice of the EEG channel and sampling frequency is based on the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifications.

Harnessing Brain Signals for Communication

ASHA Leader ◽  
2013 ◽  
Vol 18 (1) ◽  
Author(s):  
Yael Arbel
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Signals ◽  
Interface System

The P300 Brain Computer Interface system converts people’s brain signals into words on a screen, enabling people who are completely paralyzed to communicate. The system records and analyzes brain activity in real time as the user focuses on conveying the intended message. The researchers behind this technology are working to develop a simplified version for efficient home use.

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.

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
M. K.M Rahman ◽  
Md. A. Mannan Joadder
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Quantitative Comparison ◽  
Computer Interface ◽  
Eeg Signal ◽  
Brain Signals ◽  
One Step ◽  
Overall Performance ◽  
Processing Steps ◽  
Bci System

Motor Imagery (MI) is a voluntary modulation of brain signals for specific action without real limb movement. It is essential to classify MI signal to design a brain computer interface (BCI). BCI involves a number of signal processing steps, and a lot of techniques have been developed for each step. There can be numerous combinations of these techniques at different steps that can be employed to design a BCI. This work focuses on MI-based BCI using EEG signal and reviews the existing techniques. More importantly, a detailed comparative study is performed to explore the important combinations of methods by comparing their performance quantitatively. Often a method, which performs very good in one combination, can be bad performer in other combinations and it is a dilemma for the researchers to select appropriate methods for their desired BCI application.In our performance analysis, we have systematically included the variations of methods in each step of BCI such that it gives idea to BCI researchers how each method in one step fits best with specific combinations of methods in other steps. We have shown that how much each step is sensitive towards overall performance of the BCI system.We hope that this work helps, especially for new researchers, to provide a better guideline for designing more efficient BCI system.

scholarly journals Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Nurullah Akkaya ◽  
Ersin Aytac ◽  
Irfan Günsel ◽  
Ahmet Çağman
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Computer Interface ◽  
Human Control ◽  
Fuzzy Neural ◽  
Physically Disabled People

The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

scholarly journals Brain–Computer Interface Spellers: A Review

2018 ◽  
Vol 8 (4) ◽  
pp. 57 ◽  
Author(s):  
Aya Rezeika ◽  
Mihaly Benda ◽  
Piotr Stawicki ◽  
Felix Gembler ◽  
Abdul Saboor ◽  
...  
Keyword(s):  
User Interfaces ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signal ◽  
Brain Signals ◽  
Signal Features ◽  
Communication Method ◽  
Electroencephalogram Eeg

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

scholarly journals Deep Learning Algorithm for Brain-Computer Interface

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Asif Mansoor ◽  
Muhammad Waleed Usman ◽  
Noreen Jamil ◽  
M. Asif Naeem
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Noninvasive Technique ◽  
Deep Learning Algorithm ◽  
Brain Signals ◽  
Learning Techniques ◽  
Electrical Devices ◽  
The Brain

Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

Sign in / Sign up

Export Citation Format

Share Document