scholarly journals The Possibility of Using BCI Applications in Physiotherapy

2019 ◽  
Vol 292 ◽  
pp. 01033
Author(s):  
Zuzana Koudelkova ◽  
Sarka Dankova ◽  
Michal Filip ◽  
Marcela Dabrovska
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural System ◽  
Computer Interface ◽  
Experimental Part ◽  
Applied Informatics ◽  
Near Future ◽  
Bci System

Brain-Computer Interface (BCI) is an interface connecting the human neural system and computer. This article explains the fundamental principles of BCI and devices, which can be controlled using electroencephalography (EEG). Firstly, this article describes Brain-Computer interface according to obtaining brain activity. After that, the applications of BCI are proposed, which can be used in clinical practice. In the experimental part, the external systems are defined. These external systems are operated by BCI technology. This technology is developed at the Department of Informatics and Artificial Intelligence of the Faculty of Applied Informatics, Tomas Bata University in Zlin. This BCI system contains EEG technology, which is responsible for scanning a brain activity with a fourteen-channel device developed by Emotiv company. In the near future, this design of peripheral systems can be involved in clinical practice in various medical branches, especially physiotherapy.

scholarly journals Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Pablo Martinez ◽  
Hovagim Bakardjian ◽  
Andrzej Cichocki
Keyword(s):  
Real Time ◽  
Healthy Subjects ◽  
High Speed ◽  
High Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Optimal Frequency ◽  
Computer Screen ◽  
Near Future ◽  
Bci System

We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

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 A Review of Hybrid Brain-Computer Interface Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Setare Amiri ◽  
Reza Fazel-Rezai ◽  
Vahid Asadpour
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Activity Patterns ◽  
Computer Interface ◽  
Advantages And Disadvantages ◽  
Research Activities ◽  
Information Transfer Rate ◽  
Bci System

Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

scholarly journals On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2856 ◽  
Author(s):  
Soo-In Choi ◽  
Chang-Hee Han ◽  
Ga-Young Choi ◽  
Jaeyoung Shin ◽  
Kwang Soup Song ◽  
...  
Keyword(s):  
Mental Arithmetic ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Brain Area ◽  
Computer Interface ◽  
Activity Increase ◽  
Eyes Closed ◽  
Eyes Open ◽  
External Stimuli ◽  
Bci System

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

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.

scholarly journals Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

2021 ◽  
Author(s):  
Yiwen Wang ◽  
Yuxiao Lin ◽  
Chao Fu ◽  
Zhihua Huang ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural Mechanisms ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Signals ◽  
Chicken Game ◽  
Common Response ◽  
Tit For Tat ◽  
Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

scholarly journals Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

2019 ◽  
Author(s):  
Jennifer Stiso ◽  
Marie-Constance Corsi ◽  
Javier Omar Garcia ◽  
Jean M Vettel ◽  
Fabrizio De Vico Fallani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Distributed Networks ◽  
Motor Dysfunction ◽  
Computer Interface ◽  
Specific Method ◽  
Temporal Expression ◽  
And Behavior ◽  
Brain Behavior

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

scholarly journals The 2019 yearbook of Neurorestoratology

2020 ◽  
Vol 08 (01) ◽  
pp. 1-11
Author(s):  
Hongyun Huang
Keyword(s):  
Clinical Practice ◽  
Neurological Diseases ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Cell Therapies ◽  
Recent Advances

Time is infinite movement in constant motion. We are glad to see that Neurorestoratology, a new discipline, has grown into a rich field involving many global researchers in recent years. In this 2019 yearbook of Neurorestoratology, we introduce the most recent advances and achievements in this field, including findings on the pathogenesis of neurological diseases, neurorestorative mechanisms, and clinical therapeutic achievements globally. Many patients have benefited from treatments involving cell therapies, neurostimulation/neuromodulation, brain–computer interface, neurorestorative surgery or pharmacy, and many others. Clinical physicians can refer to this yearbook with the latest knowledge and apply it to clinical practice.

scholarly journals A Survey of Interactive Systems based on Brain-Computer Interfaces

2013 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Luiz Stamatto Ferreira ◽  
Leonardo Cunha de Miranda ◽  
Erica Esteves Cunha de Miranda ◽  
Sarah Gomes Sakamoto
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Research Area ◽  
Interactive Systems ◽  
Computer Interface ◽  
Immersive Environments ◽  
User Interactions ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Near Future

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

scholarly journals Could Brain–Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?

2021 ◽  
Vol 15 ◽  
Author(s):  
Stuti Chakraborty ◽  
Gianluca Saetta ◽  
Colin Simon ◽  
Bigna Lenggenhager ◽  
Kathy Ruddy
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Treatment Options ◽  
Body Representation ◽  
Vestibular Stimulation ◽  
Current Treatment ◽  
Substantial Improvement ◽  
Computer Interface ◽  
Effective Form ◽  
Brain Correlates

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person’s anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain–computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

Sign in / Sign up

Export Citation Format

Share Document