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.

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.

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.

scholarly journals Detection of attention and meditation state-based brainwave system to control prosthetic arm

2019 ◽  
Vol 13 (2) ◽  
pp. 794
Author(s):  
Ahmad Danial Abdul Rahman ◽  
Hanim Hussin
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Brain Computer Interfaces ◽  
Brain Machine Interfaces ◽  
Motor Disabilities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
3D Printed ◽  
Data Acquisition Device ◽  
The Brain

<span>Neurotechnology has led to the development of Brain-Computer Interfaces (BCIs) or Brain-Machine Interfaces (BMIs) which are devices that use brain transmission signal to operate. Electroencephalography (EEG) is one of the recent methods that could retrieve transmission signal of the brain from scalp safely. This paper will discuss the development of Neuroprosthetics limb by using patients’ attention and meditation level to produce movement. The main objective of this project is to restore mobility of patients that have suffered from motor disabilities. This project is carried out by interfacing the data acquisition device which is NeuroSky Mindwaves Headset with the microcontroller to move the prosthetic arm as the output. Arduino Nano microcontroller acts as data processing and a controller to the arm as the output. The prosthetic arm is designed by using SOLIDWORKS software and fabricated by 3D printed. From this project, the user will be able to control the prosthetic arm ranging from rotating the hand to bending the fingers creating a grasp and release gesture.</span>

Electrocorticographically controlled brain–computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants

2007 ◽  
Vol 106 (3) ◽  
pp. 495-500 ◽  
Author(s):  
Elizabeth A. Felton ◽  
J. Adam Wilson ◽  
Justin C. Williams ◽  
P. Charles Garell
Keyword(s):  
Computer Interface ◽  
Brain Areas ◽  
Motor Disabilities ◽  
Cursor Control ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Severe Motor ◽  
Subdural Electrode ◽  
Sensory Imagery

✓Brain–computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document