Analysis of Visual Sensory Processing in the Brain and Brain-Computer Interfaces for Human Attention Control

2021 ◽  
pp. 303-350
Author(s):  
Alexander E. Hramov ◽  
Alexey A. Koronovskii ◽  
Valeri A. Makarov ◽  
Vladimir A. Maksimenko ◽  
Alexey N. Pavlov ◽  
...  
Keyword(s):  
Sensory Processing ◽  
Attention Control ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Human Attention ◽  
The Brain

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. 

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 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>

BCI, BNCI, and BMI

2014 ◽  
pp. 49-62
Keyword(s):  
Emotional State ◽  
People With Disabilities ◽  
Emerging Technology ◽  
Brain Computer Interfaces ◽  
Robotic Arms ◽  
Smart Wheelchairs ◽  
Computer Interfaces ◽  
The Brain ◽  
Computer Interaction

Brain Computer Interaction and Interfaces is another emerging technology that is presenting interesting potential for people with disabilities in their interaction with computers and machines in general. Combined with robotic arms, it can lead to prosthetic arms that can be handled though the brain like a natural hand. Apart from that, brain-computer interfaces can be used for a variety of other control operations over computers and machines in general and also for detecting the emotional state of a person. People with disabilities (and not only them) could use them to interact with a range of machines from traditional PCs to smart wheelchairs that will be able not only to accept commands but understand their emotions too. Therefore, this chapter is devoted to presenting recent efforts in the area and discussing issues and challenges that lie ahead in the domain of BCI so that the reader can get an overview of this new exciting technology.

App Stores for the Brain : Privacy and Security in Brain-Computer Interfaces

2015 ◽  
Vol 34 (2) ◽  
pp. 32-39 ◽  
Author(s):  
Tamara Bonaci ◽  
Ryan Calo ◽  
Howard Jay Chizeck
Keyword(s):  
Brain Computer Interfaces ◽  
Privacy And Security ◽  
Computer Interfaces ◽  
The Brain

scholarly journals Some Computational Aspects of the Brain Computer Interfaces Based on Inner Music

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Wlodzimierz Klonowski ◽  
Wlodzisław Duch ◽  
Aleksandar Perovic ◽  
Aleksandar Jovanovic
Keyword(s):  
Natural Language ◽  
Information Capacity ◽  
Brain Computer Interfaces ◽  
Command Language ◽  
Transfer Rates ◽  
Computer Interfaces ◽  
Computational Aspects ◽  
The Brain ◽  
High Information ◽  
As If

We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the natural language. Moreover, this approach could be used to test musical abilities. Such BCI interface could be superior when there is a need for a broader command language. Some computational estimates and unresolved difficulties are presented.

Optimizing the Usability of Brain-Computer Interfaces

2018 ◽  
Vol 30 (5) ◽  
pp. 1323-1358 ◽  
Author(s):  
Yin Zhang ◽  
Steve M. Chase
Keyword(s):  
Neural Activity ◽  
Skill Learning ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Biomimetic Interfaces ◽  
Extended Practice ◽  
Definition Of ◽  
Open Question ◽  
The Brain

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Brain–Computer Interfaces and Neurofeedback for Enhancing Human Performance

2019 ◽  
pp. 125-141
Author(s):  
Ranganatha Sitaram ◽  
Andrea Sánchez Corzo ◽  
Mariana Zurita ◽  
Constanza Levican ◽  
Daniela Huepe-Artigas ◽  
...  
Keyword(s):  
Human Performance ◽  
The Body ◽  
Measured Quantity ◽  
Action Perception ◽  
Brain Computer Interfaces ◽  
Healthy Individuals ◽  
Cognition And Emotion ◽  
Brain Functions ◽  
Computer Interfaces ◽  
The Brain

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), are a group of experimental procedures in which an external sensor is used to provide information about a specific brain process in order to change the measured quantity. A BCI acquires signals from the brain of a human or an animal using any one or more of these sensors, then selects or extracts specific features of interest from the signal and converts and then translates these into artificial output that can act on the body or the outside world. A BCI may influence human performance by replacing, restoring, supplementing, or enhancing brain function. In this chapter, we discuss the extant research in terms of experimental work and neuroscience understanding of the application of BCIs and neurofeedback systems in influencing human performance in different brain functions, namely, action, perception, cognition, and emotion, in healthy individuals, expert performers, and patients.

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