scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

scholarly journals Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes

2020 ◽  
Vol 37 (5) ◽  
pp. 831-837
Author(s):  
Mesut Melek ◽  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
Information Transfer ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Digital Signals ◽  
Eeg Signals ◽  
The Gaze ◽  
Information Transfer Rate ◽  
The Brain ◽  
Bci System

Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

scholarly journals Home Automation using Brain Computer Interface for the Physically Challenged

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.

scholarly journals Automated Artifact Removal in EEG Signals of Brain Computer Interface using Wavelets and ICA

2019 ◽  
Vol 8 (12) ◽  
pp. 896-909
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Control Line ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Natural Sources ◽  
Control Signals ◽  
The Mind ◽  
Electroencephalogram Eeg ◽  
The Brain

A brain-computer interface (BCI) gives a correspondence channel that interconnects the mind with an outside device. The most generally utilized system for getting BCI control signals from the brain is the electroencephalogram (EEG). In the proposed paper, BCI framework towards an EEG chronicles are reviewed into and found that the expansion of a counterfeit motion toward it, which is brought about by eye flickers, eye development, muscle and cardiovascular commotion, just as non-natural sources (e.g., control line clamor). According to the writing survey it is discovered that these issues can be overwhelmed by utilizing mix of wavelet deterioration, independent component analysis (ICA), and thresholding

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.

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

Sensors ◽  
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.

Practical Artifact Removal Brain-Computer Interface System

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.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals A Synchronized Hybrid Brain-Computer Interface System for Simultaneous Detection and Classification of Fusion EEG Signals

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dalin Yang ◽  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Technology Use ◽  
Simple Structure ◽  
Brain Computer Interface ◽  
Simultaneous Detection ◽  
High Accuracy ◽  
Computer Interface ◽  
Eeg Signals ◽  
Fast Independent Component Analysis ◽  
Bci System ◽  
Mental Tasks

Brain-computer interface (BCI) technology represents a fast-growing field of research and applications for disabled and healthy people, which is a direct communication pathway to translate the neural information into an active command. Owing to the complicated headset structure, low accuracies, extended training periods, and nonstationary noises, BCI still has many challenges that should be dealt with for further facilitation of BCI technology use in daily life. In this study, a simplified synchronized hybrid BCI system is proposed for multiple command control by the electroencephalograph (EEG) signals in the motor cortex. This system can detect the single motor imagery (MI) task, single steady-state visually evoked potential (SSVEP) task, and hybrid MI + SSVEP tasks simultaneously (total ten mental tasks) via 2 EEG channels with high accuracy. The fast independent component analysis algorithm is employed to hybrid signals for obtaining clear EEG signals resulting from denoising. Feature extraction is performed by the wavelet transform, which is extracted by the features in the frequency and time domains. Furthermore, a four-layer convolutional neural network (CNN) is used as a classifier to distinguish different mental tasks. Finally, the hybrid MI + SSVEP system with a simple structure achieves a high accuracy of 95.56%. Additionally, the single MI-based and the SSVEP-based BCI system obtain the classification accuracy of 90.16% and 93.21%, respectively. Experimental results indicate that the synchronized hybrid BCI system could achieve multiple command control with a simple structure. In comparison with the single MI-based and the SSVEP-based BCI system, the hybrid MI + SSVEP BCI system shows a stable performance and higher efficiency. The proposed investigation provides a new method for the multiple command control by a hybrid BCI system. Also, the proposed BCI system offers the possibility of friendly utilization for disabled people because of its reliability, ease of use, and simplified headset structure.

Designing Better, Cost-Effective Brain–Computer Interfaces

2015 ◽  
Vol 23 (4) ◽  
pp. 13-19 ◽  
Author(s):  
Chang S. Nam ◽  
Matthew Moore ◽  
Inchul Choi ◽  
Yueqing Li
Keyword(s):  
Steady State ◽  
Research And Development ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Cost Effective ◽  
Computer Interface ◽  
Computer Interfaces ◽  
The Brain ◽  
Bci System ◽  
Users With Special Needs

Despite the increase in research interest in the brain–computer interface (BCI), there remains a general lack of understanding of, and even inattention to, human factors/ergonomics (HF/E) issues in BCI research and development. The goal of this article is to raise awareness of the importance of HF/E involvement in the emerging field of BCI technology by providing HF/E researchers with a brief guide on how to design and implement a cost-effective, steady-state visually evoked potential (SSVEP)–based BCI system. We also discuss how SSVEP BCI systems can be improved to accommodate users with special needs.

Sign in / Sign up

Export Citation Format

Share Document