scholarly journals Brain Computer Interface Controlled Voice Assistant

2020 ◽  
Vol 8 (6) ◽  
pp. 2370-2377
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Project Work ◽  
Daily Lives ◽  
Computer Interfaces ◽  
The Disabled ◽  
And Performance ◽  
The Brain ◽  
Bci System

A brain-controlled robot using brain computer interfaces (BCIs) was explored in this project. BCIs are systems that are able to circumvent traditional communication channels (i.e. muscles and thoughts), to ensure the human brain and physical devices communicate directly and are in charge by converting various patterns of brain activity to instructions in real time. An automation can be managed with these commands. The project work seeks to build and monitor a program that can help the disabled people accomplish certain activities independently of others in their daily lives. Develop open-source EEG and brain-computer interface analysis software. The quality and performance of BCI of different EEG signals are compared. Variable signals obtained through MATLAB Processing from the Brainwave sensor. Automation modules operate by means of the BCI system. The Brain Computer Interface aims to build a fast and reliable link between a person's brain and a personal computer. The controls also use the Brain-Computer Interface for home appliances. The system will integrate with any smartphones voice assistant.

scholarly journals Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

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.

scholarly journals Brain-Computer Interfaces for Internet of Things

2018 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

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.

Brain Computer Interfaces for Silent Speech

2016 ◽  
Vol 25 (2) ◽  
pp. 208-230 ◽  
Author(s):  
Yousef Rezaei Tabar ◽  
Ugur Halici
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Speech Communication ◽  
Disabled People ◽  
Computer Interfaces ◽  
Life Problems ◽  
Signal Processing Methods ◽  
Silent Speech ◽  
Bci System ◽  
Different Parts

Brain Computer Interface (BCI) systems provide control of external devices by using only brain activity. In recent years, there has been a great interest in developing BCI systems for different applications. These systems are capable of solving daily life problems for both healthy and disabled people. One of the most important applications of BCI is to provide communication for disabled people that are totally paralysed. In this paper, different parts of a BCI system and different methods used in each part are reviewed. Neuroimaging devices, with an emphasis on EEG (electroencephalography), are presented and brain activities as well as signal processing methods used in EEG-based BCIs are explained in detail. Current methods and paradigms in BCI based speech communication are considered.

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 THE IMPACT OF ADVERTISEMENT ON CONSUMER’S PERCEPTION

2017 ◽  
Vol 5 ◽  
pp. 187-191
Author(s):  
Martin Hudák ◽  
Radovan MadleĹˆĂˇk ◽  
Veronika Brezániová
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Consumer Perception ◽  
Computer Interface ◽  
Market Situation ◽  
And Behavior ◽  
The Impact ◽  
The Brain ◽  
Tracking Technology ◽  
Wireless Eeg

Marketing can be described as a tool for companies to influence the consumer’s perception to the desired direction. The current market situation is characterized by dynamism, growing consumer power, and intense competition. The consumer perception and behavior are changing and therefore need to be constantly monitored and measured. The aim of this article is to scan and measure consumer’s perception while watching a video advertisement. During this experiment, an eye-tracking technology was used, which allows capturing a consumer’s gaze. The central part of the research is to measure the brain activity of a consumer based on the EEG (Electroencephalography). EMOTIV Epoc+ is a 14-channel wireless EEG, designed for contextualized research and advanced brain computer interface applications. An advertising campaign from four different mobile operators was used for this purpose. In the conclusion of this article, consumer’s perception of different advertising campaigns are compared and evaluated.

scholarly journals A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

2020 ◽  
Vol 10 (10) ◽  
pp. 734
Author(s):  
Md Rakibul Mowla ◽  
Jesus D. Gonzalez-Morales ◽  
Jacob Rico-Martinez ◽  
Daniel A. Ulichnie ◽  
David E. Thompson
Keyword(s):  
Discriminant Analysis ◽  
System Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Classification Technique ◽  
Latency Jitter ◽  
Bci System

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

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 Translation of Brain Activity Patterns of a user into Commands using Electroencephalography (EEG)

2021 ◽  
pp. 62-68
Author(s):  
Ranjana B. Jadekar ◽  
A. R. Sindhu ◽  
M. T. Vinay
Keyword(s):  
Environmental Control ◽  
Brain Activity ◽  
Activity Patterns ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Control Option ◽  
Electrophysiological Signals ◽  
Interactive Application ◽  
The Brain ◽  
Bci System

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). The BCI system uses oscillatory Electroencephalography (EEG) signals, recorded using specific mental activity, as input and provides a control option by its output. A brain-computer interface uses electrophysiological signals to control the remote devices. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. The computer processes the EEG signals and uses it in order to accomplish tasks such as communication and environmental control.

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