Brain–Computer Interfaces for Movement

2021 ◽  
pp. 82-111
Author(s):  
Walter Glannon
Keyword(s):  
Motor Output ◽  
Shared Control ◽  
Brain Computer Interfaces ◽  
Physical Movement ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Degree Of Control ◽  
Mental Acts ◽  
Bodily Movements

This chapter describes differences between passive and active brain–computer interfaces (BCIs). It explains how active BCIs enable users to move a prosthetic arm or limb, or a computer cursor, and gives them a certain degree of control over these movements. There is shared control between the user and the interface, and this restores the user’s capacity for agency. In normal voluntary bodily movements, one does not have to think about performing them. In BCI-mediated movements, the user must plan how to use the system in activating and directing brain signals to the computer to perform them. There are two intentions: intending to perform an action; and intending to perform it with a BCI. There are two mental acts: activating and directing signals to the computer to produce the motor output. The fact that there are two intentions and two mental acts resulting in a physical movement could motivate a revision of moral and legal criteria of responsibility for BCI users. It could influence judgements of responsibility for actions, omissions, and their consequences.

scholarly journals An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 560
Author(s):  
Andrea Bonci ◽  
Simone Fiori ◽  
Hiroshi Higashi ◽  
Toshihisa Tanaka ◽  
Federica Verdini
Keyword(s):  
Biomedical Engineering ◽  
Data Privacy ◽  
Lie Detection ◽  
Human Cognition ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Ethical And Legal Issues ◽  
And Performance ◽  
Brain Computer Interfacing

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

scholarly journals The current state of electrocorticography-based brain–computer interfaces

2020 ◽  
Vol 49 (1) ◽  
pp. E2 ◽  
Author(s):  
Kai J. Miller ◽  
Dora Hermes ◽  
Nathan P. Staff
Keyword(s):  
Functional Limitations ◽  
Brain Computer Interfaces ◽  
Brain Surface ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Current State ◽  
Temporal And Spatial ◽  
Direct Feedback ◽  
The Brain ◽  
Lateral Sclerosis

Brain–computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.

scholarly journals Evolution of brain-computer interfaces: going beyond classic motor physiology

2009 ◽  
Vol 27 (1) ◽  
pp. E4 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Gerwin Schalk ◽  
Jarod Roland ◽  
Adam Rouse ◽  
Daniel W. Moran
Keyword(s):  
Primary Motor Cortex ◽  
Current Status ◽  
Brain Computer Interfaces ◽  
Motor Disabilities ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Motor Physiology ◽  
Potential Efficacy ◽  
Technical Possibility ◽  
Cortical Physiology

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

Learning from brain control: clinical application of brain–computer interfaces

e-Neuroforum ◽  
2015 ◽  
Vol 21 (4) ◽  
Author(s):  
Niels Birbaumer ◽  
Ujwal Chaudhary
Keyword(s):  
Clinical Application ◽  
Motor Function ◽  
Brain Activity ◽  
Chronic Stroke ◽  
Skill Learning ◽  
Brain Computer Interfaces ◽  
Brain Machine Interfaces ◽  
Positive Effects ◽  
Computer Interfaces ◽  
Brain Signals

AbstractBrain-computer interfaces (BCI) use neuroelectric and metabolic brain activity to activate peripheral devices and computers without mediation of the motor system. In order to activate the BCI patients have to learn a certain amount of brain control. Self-regulation of brain activity was found to follow the principles of skill learning and instrumental conditioning. This review focuses on the clinical application of brain-computer interfaces in paralyzed patients with locked-in syndrome and completely locked-in syndrome (CLIS). It was shown that electroencephalogram (EEG)-based brain-computer interfaces allow selection of letters and words in a computer menu with different types of EEG signals. However, in patients with CLIS without any muscular control, particularly of eye movements, classical EEG-based brain-computer interfaces were not successful. Even after implantation of electrodes in the human brain, CLIS patients were unable to communicate. We developed a theoretical model explaining this fundamental deficit in instrumental learning of brain control and voluntary communication: patients in complete paralysis extinguish goal-directed responseoriented thinking and intentions. Therefore, a reflexive classical conditioning procedure was developed and metabolic brain signals measured with near infrared spectroscopy were used in CLIS patients to answer simple questions with a “yes” or “no”-brain response. The data collected so far are promising and show that for the first time CLIS patients communicate with such a BCI system using metabolic brain signals and simple reflexive learning tasks. Finally, brain machine interfaces and rehabilitation in chronic stroke are described demonstrating in chronic stroke patients without any residual upper limb movement a surprising recovery of motor function on the motor level as well as on the brain level. After extensive combined BCI training with behaviorally oriented physiotherapy, significant improvement in motor function was shown in this previously intractable paralysis. In conclusion, clinical application of brain machine interfaces in well-defined and circumscribed neurological disorders have demonstrated surprisingly positive effects. The application of BCIs to psychiatric and clinical-psychological problems, however, at present did not result in substantial improvement of complex behavioral disorders.

The Application of BCI Technology in Android RPG Game

2014 ◽  
Vol 496-500 ◽  
pp. 2015-2018
Author(s):  
Jing Hai Yin ◽  
Zheng Dong Mu ◽  
Jian Feng Hu
Keyword(s):  
Brain Computer Interface ◽  
Human Interaction ◽  
Research Interest ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals

To enhance human interaction with machines, research interest is growing to develop a Brain-Computer Interface (BCI), which allows communication of a human with a machine only by use of brain signals. In this paper, one type of android RPG game was designed for application of brain computer interfaces.

scholarly journals Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces

2020 ◽  
Vol 10 (10) ◽  
pp. 687 ◽  
Author(s):  
Zhipeng He ◽  
Zina Li ◽  
Fuzhou Yang ◽  
Lei Wang ◽  
Jingcong Li ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Experimental Results ◽  
Brain Computer Interfaces ◽  
Continuous Development ◽  
Research Directions ◽  
Sensory Stimuli ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Multimodal Emotion Recognition

With the continuous development of portable noninvasive human sensor technologies such as brain–computer interfaces (BCI), multimodal emotion recognition has attracted increasing attention in the area of affective computing. This paper primarily discusses the progress of research into multimodal emotion recognition based on BCI and reviews three types of multimodal affective BCI (aBCI): aBCI based on a combination of behavior and brain signals, aBCI based on various hybrid neurophysiology modalities and aBCI based on heterogeneous sensory stimuli. For each type of aBCI, we further review several representative multimodal aBCI systems, including their design principles, paradigms, algorithms, experimental results and corresponding advantages. Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI.

Sign in / Sign up

Export Citation Format

Share Document