scholarly journals Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Usman Salahuddin ◽  
Pu-Xian Gao
Keyword(s):  
Signal Generation ◽  
Signal Acquisition ◽  
Brain Machine Interfaces ◽  
Prosthetic Devices ◽  
Hybrid Techniques ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Commercial Market ◽  
Neural Signaling ◽  
The Brain

Brain machine interfaces (BMIs), or brain computer interfaces (BCIs), are devices that act as a medium for communications between the brain and the computer. It is an emerging field with numerous applications in domains of prosthetic devices, robotics, communication technology, gaming, education, and security. It is noted in such a multidisciplinary field, many reviews have surveyed on various focused subfields of interest, such as neural signaling, microelectrode fabrication, and signal classification algorithms. A unified review is lacking to cover and link all the relevant areas in this field. Herein, this review intends to connect on the relevant areas that circumscribe BMIs to present a unified script that may help enhance our understanding of BMIs. Specifically, this article discusses signal generation within the cortex, signal acquisition using invasive, non-invasive, or hybrid techniques, and the signal processing domain. The latest development is surveyed in this field, particularly in the last decade, with discussions regarding the challenges and possible solutions to allow swift disruption of BMI products in the commercial market.

Neuroethics and Implanted Brain Machine Interfaces

2013 ◽  
pp. 351-365
Author(s):  
Ellen M. McGee
Keyword(s):  
Legal Issues ◽  
The Internet ◽  
Brain Machine Interfaces ◽  
Computer Interfaces ◽  
Scientific Organizations ◽  
Human Ability ◽  
Ethical And Legal Issues ◽  
The Individual ◽  
The Brain ◽  
Radical Enhancement

Transformations of humans through advances in bioelectronics, nanotechnologies, and computer science are leading to hybrids of humans and machines. Future brain-machine interfaces will enable humans not only to be constantly linked to the Internet, and to cyber think, but will also enable technology to take information directly from the brain. Brain-computer interfaces, where a chip is implanted in the brain, will facilitate a tremendous augmentation of human capacities, including the radical enhancement of the human ability to remember and to reason, and to achieve immortality through cloning and brain downloading, or existence in virtual reality. The ethical and legal issues raised by these possibilities represent global challenges. The most pressing concerns are those raised by privacy and autonomy. The potential exists for control of persons, through global tracking, by actually “seeing” and “hearing” what the individual is experiencing, and by controlling and directing an individual’s thoughts, emotions, moods, and motivations. Public dialogue must be initiated. New principles, agencies, and regulations need to be formulated and scientific organizations, states, countries, and the United Nations must all be involved.

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>

scholarly journals Defining Surgical Terminology and Risk for Brain Computer Interface Technologies

2021 ◽  
Vol 15 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Daniel W. Moran ◽  
Tim R. Mullen
Keyword(s):  
Brain Volume ◽  
Form Factors ◽  
Risk Profile ◽  
The Body ◽  
Clinical Population ◽  
Semantic Framework ◽  
Non Invasive ◽  
Computer Interfaces ◽  
The Common ◽  
The Brain

With the emergence of numerous brain computer interfaces (BCI), their form factors, and clinical applications the terminology to describe their clinical deployment and the associated risk has been vague. The terms “minimally invasive” or “non-invasive” have been commonly used, but the risk can vary widely based on the form factor and anatomic location. Thus, taken together, there needs to be a terminology that best accommodates the surgical footprint of a BCI and their attendant risks. This work presents a semantic framework that describes the BCI from a procedural standpoint and its attendant clinical risk profile. We propose extending the common invasive/non-invasive distinction for BCI systems to accommodate three categories in which the BCI anatomically interfaces with the patient and whether or not a surgical procedure is required for deployment: (1) Non-invasive—BCI components do not penetrate the body, (2) Embedded—components are penetrative, but not deeper than the inner table of the skull, and (3) Intracranial –components are located within the inner table of the skull and possibly within the brain volume. Each class has a separate risk profile that should be considered when being applied to a given clinical population. Optimally, balancing this risk profile with clinical need provides the most ethical deployment of these emerging classes of devices. As BCIs gain larger adoption, and terminology becomes standardized, having an improved, more precise language will better serve clinicians, patients, and consumers in discussing these technologies, particularly within the context of surgical procedures.

scholarly journals Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations

2018 ◽  
Vol 25 (2) ◽  
pp. 139-154 ◽  
Author(s):  
Marc W. Slutzky
Keyword(s):  
Brain Machine Interfaces ◽  
Brain Physiology ◽  
The Past ◽  
Computer Interfaces ◽  
Cord Injury ◽  
Behavior Learning ◽  
Primary Focus ◽  
The Brain ◽  
Lateral Sclerosis

Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment. Because BMIs are controlled directly by the activity of prespecified neurons or cortical areas, they also provide a powerful paradigm with which to investigate fundamental questions about brain physiology, including neuronal behavior, learning, and the role of oscillations. This article reviews the clinical and neuroscientific applications of BMIs, with a primary focus on motor BMIs.

scholarly journals Summary of over Fifty Years with Brain-Computer Interfaces—A Review

2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Aleksandra Kawala-Sterniuk ◽  
Natalia Browarska ◽  
Amir Al-Bakri ◽  
Mariusz Pelc ◽  
Jaroslaw Zygarlicki ◽  
...  
Keyword(s):  
Science Fiction ◽  
Technological Development ◽  
Brain Activity ◽  
Signal Acquisition ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
History Of ◽  
Common Effort ◽  
Signal Acquisition And Processing ◽  
The Brain

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

scholarly journals Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Ji-Hoon Jeong ◽  
Jeong-Hyun Cho ◽  
Kyung-Hwan Shim ◽  
Byoung-Hee Kwon ◽  
Byeong-Hoo Lee ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Critical Issue ◽  
Classification Performance ◽  
Non Invasive ◽  
Real Movement ◽  
Multimodal Signals ◽  
Computer Interfaces ◽  
Number Of Classes ◽  
The Brain ◽  
Healthy Participants

Abstract Background Non-invasive brain–computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. Findings We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. Conclusions The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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 fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

In vivo exploration of brain phosphorus 31 metabolism in patients with senile dementia of Alzheimer type

1994 ◽  
Vol 9 (2) ◽  
pp. 105-109
Author(s):  
G Mecheri ◽  
Y Bissuel ◽  
J Dalery ◽  
JL Terra ◽  
G Balvay ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Senile Dementia ◽  
Phospholipid Metabolism ◽  
Alzheimer Type ◽  
Dementia Of Alzheimer Type ◽  
Non Invasive ◽  
Significant Difference ◽  
The Brain

SummaryIn vivo NMR 31p spectroscopy is a non invasive, non ionizing method of exploration of energy and phospholipid metabolism in the brain. This study consisted of comparing 31p spectra in five patients with Senile Dementia of Alzheimer Type (SDAT) with those of four controls of similar ages. Abnormal phosphonionocsters (PME) concentrations, either high or low, were found in the patients, but statistical analysis did not elicit any significant difference relative to controls.

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