Brain-computer interfaces: military, neurosurgical, and ethical perspective

2010 ◽  
Vol 28 (5) ◽  
pp. E25 ◽  
Author(s):  
Ivan S. Kotchetkov ◽  
Brian Y. Hwang ◽  
Geoffrey Appelboom ◽  
Christopher P. Kellner ◽  
E. Sander Connolly
Keyword(s):  
Performance Enhancement ◽  
Brain Computer Interfaces ◽  
Military Operations ◽  
Ethical Perspective ◽  
Computer Interfaces ◽  
Robotic Devices ◽  
And Performance ◽  
Developing Area ◽  
Clinical Duty ◽  
The Military

Brain-computer interfaces (BCIs) are devices that acquire and transform neural signals into actions intended by the user. These devices have been a rapidly developing area of research over the past 2 decades, and the military has made significant contributions to these efforts. Presently, BCIs can provide humans with rudimentary control over computer systems and robotic devices. Continued advances in BCI technology are especially pertinent in the military setting, given the potential for therapeutic applications to restore function after combat injury, and for the evolving use of BCI devices in military operations and performance enhancement. Neurosurgeons will play a central role in the further development and implementation of BCIs, but they will also have to navigate important ethical questions in the translation of this highly promising technology. In the following commentary the authors discuss realistic expectations for BCI use in the military and underscore the intersection of the neurosurgeon's civic and clinical duty to care for those who serve their country.

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 Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142098025
Author(s):  
Jorge Antonio Martinez-Ledezma ◽  
Jose Hugo Barron-Zambrano ◽  
Alan Diaz-Manriquez ◽  
Juan Carlos Elizondo-Leal ◽  
Vicente Paul Saldivar-Alonso ◽  
...  
Keyword(s):  
Modular Design ◽  
Time Interval ◽  
Brain Computer Interfaces ◽  
Hexapod Robot ◽  
Human Capabilities ◽  
Computer Interfaces ◽  
Communication Disabilities ◽  
Long Time ◽  
Robotic Devices ◽  
Mlp Model

Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

scholarly journals Application of Brain-computer Interfaces in Assistive Technologies

2020 ◽  
Vol 19 (2) ◽  
pp. 277-301
Author(s):  
Filipp Gundelakh ◽  
Lev Stankevich ◽  
Konstantin Sonkin ◽  
Ganna Nagornova ◽  
Natalia Shemyakina
Keyword(s):  
Brain Computer Interface ◽  
Assistive Technologies ◽  
Computer Interface ◽  
Support Vector ◽  
Bioelectrical Activity ◽  
Brain Computer Interfaces ◽  
Technology Application ◽  
Riemann Geometry ◽  
Computer Interfaces ◽  
Robotic Devices

In the paper issues of brain-computer interface applications in assistive technologies are considered in particular for robotic devices control. Noninvasive brain-computer interfaces are built based on the classification of electroencephalographic signals, which show bioelectrical activity in different zones of the brain. Such brain-computer interfaces after training are able to decode electroencephalographic patterns corresponding to different imaginary movements and patterns corresponding to different audio-visual stimulus. The requirements which must be met by brain-computer interfaces operating in real time, so that biological feedback is effective and the user's brain can correctly associate responses with events are formulated. The process of electroencephalographic signal processing in noninvasive brain-computer interface is examined including spatial and temporal filtering, artifact removal, feature selection, and classification. Descriptions and comparison of classifiers based on support vector machines, artificial neural networks, and Riemann geometry are presented. It was shown that such classifiers can provide accuracy at the level of 60-80% for recognition of imaginary movements from two to four classes. Examples of application of the classifiers to control robotic devices were presented. The approach is intended both to help healthy users to perform daily functions better and to increase the quality of life of people with movement disabilities. Tasks to increase the efficiency of technology application are formulated.

scholarly journals Artificial Intelligence in Military Application – Opportunities and Challenges

2021 ◽  
Vol 26 (2) ◽  
pp. 157-165
Author(s):  
István Szabadföldi
Keyword(s):  
Artificial Intelligence ◽  
Situational Awareness ◽  
State Of The Art ◽  
Operational Planning ◽  
Military Operations ◽  
Weapon Systems ◽  
Military Application ◽  
Human Machine Interfaces ◽  
And Performance ◽  
The Military

Abstract Artificial Intelligence (AI) is playing an increasing role in planning and supporting military operations and becoming a key tool in intelligence and analysis of the enemy’s intelligence. Another field of application of AI is the field of application of autonomous weapon systems and vehicles. The use of AI is expected to have a greater impact on the military functions of human-machine interfaces (machine-learning, man-machine teaming). AI promises to get over the “3V challenge” (volume, variety and velocity) of Big Data, and is also expected to reduce the risks concerning the other “2V” (veracity, value), and to render data processing on a controlled level of decision based on AI’s knowledge. The aim of the article is to provide an overview on the potentials of application of AI in the military and to highlight the need to identify and define measurable indicators to evaluate benefits of state-of-the-art technologies and solutions which are expected to improve quality and performance of operations focusing on key areas as of situational awareness and decision-making support and also logistic and operational planning as well as modelling and simulation (M&S).

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

Study on the Adoption of Competence as a Purpose of the Training of Forces

2020 ◽  
Vol 26 (2) ◽  
pp. 342-345
Author(s):  
Benoni Sfârlog ◽  
Ștefania Bumbuc ◽  
Constantin Grigoraș
Keyword(s):  
Professional Development ◽  
New Paradigm ◽  
And Performance ◽  
The Military

AbstractIn recent decades, a new paradigm marks the conceptual transformation through which competencies take the place of objectives in education, in general and in training and professional development, in particular. It becomes necessary and useful to analyze the necessity, possibility and opportunity of focusing the instruction on competences. Thus they acquire, in an integrative way, the triple state of a referential system for quality and performance in the military actions, of the objective of the instructive-formative process, and of the result of learning.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

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