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 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.

Using a P300 Brain–Computer Interface in an Immersive Virtual Environment

2010 ◽  
Vol 19 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Michael Donnerer ◽  
Anthony Steed
Keyword(s):  
Virtual Environment ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Disabled People ◽  
3D Objects ◽  
Immersive Virtual Environment ◽  
Computer Interfaces ◽  
3D Environment ◽  
Significant Difference ◽  
Environment Control

Brain–computer interfaces (BCIs) provide a novel form of human–computer interaction. The purpose of these systems is to aid disabled people by affording them the possibility of communication and environment control. In this study, we present experiments using a P300 based BCI in a fully immersive virtual environment (IVE). P300 BCIs depend on presenting several stimuli to the user. We propose two ways of embedding the stimuli in the virtual environment: one that uses 3D objects as targets, and a second that uses a virtual overlay. Both ways have been shown to work effectively with no significant difference in selection accuracy. The results suggest that P300 BCIs can be used successfully in a 3D environment, and this suggests some novel ways of using BCIs in real world environments.

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.

Neuroscience Technology and Interfaces for Speech, Language, and Musical Communication

2018 ◽  
pp. 5886-5900
Author(s):  
Dionysios Politis ◽  
Miltiadis Tsalighopoulos ◽  
Georgios Kyriafinis
Keyword(s):  
Hearing Aids ◽  
Brain Activity ◽  
Oral Communication ◽  
Speech Communication ◽  
Learning Potential ◽  
Great Expectations ◽  
Computer Interfaces ◽  
Language Communication ◽  
Audio Communication ◽  
Musical Communication

Medical practice is extensively using monitoring devices that are more or less invasive and immersive. For aural and oral communication these could be hearing aids, prosthetics, cochlear implants or goggles detecting vestibular effects and vertigo. Recently, a wide variety of trendy mobile or wearable devices has been offered to the general public, provoking a frenzy for augmentation alongside the great expectations that the popularization of Brain Computer Interfaces has caused to both the consumer market and the scientific community. The use of bionic devices clinched with synapses of the nerves does not merely mingle input activity to brain activity, but also it provides a virtual channel for augmenting and manipulating speech communication, language communication and even further musical communication. The electromechanical parameters, the medical practices and the learning potential for this new world of augmented Human Computer Interaction platforms and devices are examined under the prism of audio communication.

Neuroscience Technology and Interfaces for Speech, Language, and Musical Communication

2019 ◽  
pp. 403-419
Author(s):  
Dionysios Politis ◽  
Miltiadis Tsalighopoulos ◽  
Georgios Kyriafinis
Keyword(s):  
Hearing Aids ◽  
Brain Activity ◽  
Oral Communication ◽  
Speech Communication ◽  
Learning Potential ◽  
Great Expectations ◽  
Computer Interfaces ◽  
Language Communication ◽  
Audio Communication ◽  
Musical Communication

Medical practice is extensively using monitoring devices that are more or less invasive and immersive. For aural and oral communication these could be hearing aids, prosthetics, cochlear implants, or goggles detecting vestibular effects and vertigo. Recently, a wide variety of trendy mobile or wearable devices has been offered to the general public, provoking a frenzy for augmentation alongside the great expectations that the popularization of brain-computer interfaces has caused to both the consumer market and the scientific community. The use of bionic devices clinched with synapses of the nerves does not merely mingle input activity to brain activity, but also it provides a virtual channel for augmenting and manipulating speech communication, language communication, and even further, musical communication. The electromechanical parameters, the medical practices, and the learning potential for this new world of augmented human-computer interaction platforms and devices are examined under the prism of audio communication.

DESIGN OF ASSISTIVE WHEELCHAIR SYSTEM DIRECTLY STEERED BY HUMAN THOUGHTS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350013 ◽  
Author(s):  
JUNHUA LI ◽  
JIANYI LIANG ◽  
QIBIN ZHAO ◽  
JIE LI ◽  
KAN HONG ◽  
...  
Keyword(s):  
Real World ◽  
Peripheral Nerves ◽  
Brain Computer Interface ◽  
External Environment ◽  
Computer Interface ◽  
Assistive Device ◽  
Disabled People ◽  
Electroencephalogram Eeg ◽  
Bci System ◽  
Daily Application

Integration of brain–computer interface (BCI) technique and assistive device is one of chief and promising applications of BCI system. With BCI technique, people with disabilities do not have to communicate with external environment through traditional and natural pathways like peripheral nerves and muscles, and could achieve it only by their brain activities. In this paper, we designed an electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements. We evaluated the feasibility of BCI-based wheelchair in terms of accuracies and real-world testing. The results demonstrate that our BCI wheelchair is of good performance not only in accuracy, but also in practical running testing in a real environment. This fact implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.

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.

EEG-Based Brain–Computer Interfaces

2017 ◽  
Author(s):  
Gert Pfurtscheller ◽  
Clemens Brunner ◽  
Christa Neuper
Keyword(s):  
Communication Channel ◽  
Brain Activity ◽  
Clinical Applications ◽  
Current User ◽  
Physiological Signals ◽  
Computer Interface ◽  
Computer Interfaces ◽  
Control Signals ◽  
And Control ◽  
Eeg Features

A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.

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