Non-Invasive Brain-Computer Interface Technology (BCI) Modalities and Implementation: A Review Article and Assimilation of BCI Models

Brain-computer interface (BCI) technology or brain-machine interface (BMI) technology has become the most attractive field for researchers in various disciplines and has occupied an important place in many scientific and even recreational applications. This review first highlights the different and most frequently used methods for implementing brain-computer interface (BCI) systems with a focus on non-invasive BCI models. Secondly, it analyzes the different stages of building a BCI system (input stage, signal processing stage, and output stage). Then it compares the different methods in terms of the algorithms used and the pros and cons. The aim of the study is to find the most adequate and price method to record the EEG by means of electrodes placed on the scalp. Then some features will be extracted from the EEG and sent to a classifier, whose response is translated features into some action whose execution provides feedback to the user.

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Home Automation using Brain Computer Interface for the Physically Challenged

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a3002.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 5018-5021

Keyword(s):

Brain Computer Interface ◽

Brain Structures ◽

Computer Interface ◽

Brain Machine Interface ◽

Home Automation ◽

Eeg Signals ◽

Machine Interface ◽

Physically Challenged ◽

The Brain ◽

Bci System

A Brain-Computer Interface (BCI)is labeledas Mind-Machine Interface (MMI) or a Brain-Machine Interface (BMI). It affords a non-muscular channel of messagein between the computer and a human brain. Using the enhancements in interface equipment to electronics,and the necessity to helpindividuals suffering from disabilities, a new area in this study has begun by acceptingtasks of brain. The Electro-Encephalogram (EEG) is an electrical activity created by brain structures and verified from the scalp using electrodes. The EEG signal is used in actualspell to accomplishperipheral devices using a broad BCI system. The post-processed output signals are converted to suitable instructions to regulate output devices. The main seek is to aidparalyzed and physically immobilizedpersons to govern the home appliances making use of Electro-Encephalogram (EEG) signals, such that they grow to beautonomous. According to the brain responsiveness the devices can be designated then usingrelays, the switching of the home-basedmachinescan be completedconsequently.

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

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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The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽

10.3390/s21051613 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1613

Author(s):

Man Li ◽

Feng Li ◽

Jiahui Pan ◽

Dengyong Zhang ◽

Suna Zhao ◽

...

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

System Control ◽

Average Accuracy ◽

Training Samples ◽

The Disabled ◽

Limited Training Samples ◽

The Stability ◽

Electroencephalogram Eeg ◽

Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

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Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

10.1109/mmar49549.2021.9528447 ◽

2021 ◽

Author(s):

Natalia Browarska ◽

Jaroslaw Zygarlicki ◽

Mariusz Pelc ◽

Michal Niemczynowicz ◽

Malgorzata Zygarlicka ◽

...

Keyword(s):

Pilot Study ◽

Single Channel ◽

Brain Computer Interface ◽

Computer Interface ◽

Non Invasive ◽

Eeg Data

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EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Brain Sciences ◽

10.3390/brainsci8110199 ◽

2018 ◽

Vol 8 (11) ◽

pp. 199 ◽

Cited By ~ 6

Author(s):

Rodrigo Ramele ◽

Ana Villar ◽

Juan Santos

Keyword(s):

Brain Computer Interface ◽

Waveform Analysis ◽

Computer Interface ◽

Clinical Tool ◽

Non Invasive ◽

Computer Interfaces ◽

Brain Signals ◽

Public Dataset ◽

Imaging Sensor ◽

Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

Journal of Control Science and Engineering ◽

10.1155/2021/9959195 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mingwei Zhang ◽

Yao Hou ◽

Rongnian Tang ◽

Youjun Li

Keyword(s):

Extreme Learning Machine ◽

Motor Imagery ◽

Tangent Space ◽

Brain Computer Interface ◽

Covariance Matrices ◽

Computer Interface ◽

Spatial Covariance ◽

Learning Machine ◽

Low Dimensional ◽

Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

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Fuzzy logic technique to control a robot based on non-invasive brain computer interface by human group

2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) ◽

10.1109/icrom.2014.6990984 ◽

2014 ◽

Author(s):

Hassan Samadi ◽

Mahdi Karimi

Keyword(s):

Fuzzy Logic ◽

Brain Computer Interface ◽

Computer Interface ◽

Human Group ◽

Non Invasive ◽

Fuzzy Logic Technique

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Practical Artifact Removal Brain-Computer Interface System

Advances in Bioinformatics and Biomedical Engineering - Emerging Theory and Practice in Neuroprosthetics ◽

10.4018/978-1-4666-6094-6.ch013 ◽

2014 ◽

pp. 265-277

Author(s):

Wei-Yen Hsu

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Support Vector ◽

Artifact Removal ◽

Eeg Data ◽

Interface System ◽

Electroencephalogram Eeg ◽

Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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