scholarly journals COMPUTER INTERFACES OF THE BRAIN FOR NEUROREHABILITATION - ITS CURRENT STATE AS A STRATEGY FOR REHABILITATION AFTER A STROKE

2020 ◽  
Vol 35 (1) ◽  
pp. 285-300
Author(s):  
A.S.A. Al-Masri ◽  
Keyword(s):  
Brain Activity ◽  
Large Population ◽  
Mental Image ◽  
Computer Interfaces ◽  
Current State ◽  
Wide Range ◽  
Kinetic Component ◽  
Sensory Responses ◽  
Brain Reorganization ◽  
The Brain

The idea of using computer-integrated interfaces (BCIs) for rehabilitation comes relatively recently. In essence, BCI for neurological rehabilitation involves recording and decoding the patient's local brain signals when attempting to perform a task (even if incomplete), or during the creation of a mental image. The main goal is to attract individual parts of the brain and promote neuroplasticity. Recorded labeling can be used in various ways: (1) distinguishing and improving kinematic training by feedbacking the patient using a fictitious kinetic component, for example in a virtual environment; (2) achieving the desired physical activity through robotic orthotics or motivational rehabilitation. Functionality associated with the limbs of the patient - encourages and improves task performance, even "closing" a broken sensory ring, providing the patient with appropriate sensory responses; (3) understand brain reorganization after injury, influence or even measure plasticity changes in brain networks. For example, using brain stimulation to restore balance between the two hemispheres, as evidenced by a functional recording of brain activity during movement, may help recovery. Its potential benefit to patients has been demonstrated at various levels, and its versatility in frontal applications makes it adaptable to a large population. The condition and condition of many new rehabilitation systems must be assessed in relation to our current and somewhat tried-and-tested traditional methods, as well as the wide range of possible brain damage. The heterogeneity of expression after injury inevitably leads to the decoding of brain symptoms and thus its use in pathological conditions, requiring controlled clinical trials.

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 Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

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 Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

scholarly journals Association Between Trait Empathy and Resting Brain Activity in Women With Primary Dysmenorrhea During the Pain and Pain-Free Phases

2020 ◽  
Vol 11 ◽  
Author(s):  
Wanghuan Dun ◽  
Tongtong Fan ◽  
Qiming Wang ◽  
Ke Wang ◽  
Jing Yang ◽  
...  
Keyword(s):  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Primary Dysmenorrhea ◽  
Pain Experience ◽  
Current State ◽  
The Neural Network ◽  
The Right ◽  
The Brain

Empathy refers to the ability to understand someone else's emotions and fluctuates with the current state in healthy individuals. However, little is known about the neural network of empathy in clinical populations at different pain states. The current study aimed to examine the effects of long-term pain on empathy-related networks and whether empathy varied at different pain states by studying primary dysmenorrhea (PDM) patients. Multivariate partial least squares was employed in 46 PDM women and 46 healthy controls (HC) during periovulatory, luteal, and menstruation phases. We identified neural networks associated with different aspects of empathy in both groups. Part of the obtained empathy-related network in PDM exhibited a similar activity compared with HC, including the right anterior insula and other regions, whereas others have an opposite activity in PDM, including the inferior frontal gyrus and right inferior parietal lobule. These results indicated an abnormal regulation to empathy in PDM. Furthermore, there was no difference in empathy association patterns in PDM between the pain and pain-free states. This study suggested that long-term pain experience may lead to an abnormal function of the brain network for empathy processing that did not vary with the pain or pain-free state across the menstrual cycle.

scholarly journals ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving

2018 ◽  
Vol 6 (3) ◽  
pp. 127
Author(s):  
B. Naresh ◽  
S. Rambabu ◽  
D. Khalandar Basha
Keyword(s):  
Brain Activity ◽  
Traffic Signals ◽  
Electrical Signal ◽  
Emotional States ◽  
Brain Wave ◽  
Computer Interfaces ◽  
Medium Level ◽  
And Control ◽  
To Receive ◽  
The Brain

<span>This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.</span>

scholarly journals Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Santosh Chandrasekaran ◽  
Matthew Fifer ◽  
Stephan Bickel ◽  
Luke Osborn ◽  
Jose Herrero ◽  
...  
Keyword(s):  
Computer Technology ◽  
Traumatic Injury ◽  
The Body ◽  
Computer Interface ◽  
Future Directions ◽  
Historical Perspectives ◽  
Computer Interfaces ◽  
Wide Range ◽  
Study Participants ◽  
The Brain

AbstractAlmost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.

scholarly journals Do Brain Oscillations Orchestrate Memory?

2018 ◽  
Vol 4 (1) ◽  
pp. 16-33 ◽  
Author(s):  
Wenhan Luo ◽  
Ji-Song Guan
Keyword(s):  
Brain Activity ◽  
Large Population ◽  
Rhythmic Activity ◽  
Brain Oscillations ◽  
Strongly Coupled ◽  
Specific Memory ◽  
State Dependent ◽  
Neural Communication ◽  
The Brain ◽  
Oscillatory Waves

Rhythmicity and oscillations are common features in nature, and can be seen in phenomena such as seasons, breathing, and brain activity. Despite the fact that a single neuron transmits its activity to its neighbor through a transient pulse, rhythmic activity emerges from large population-wide activity in the brain, and such rhythms are strongly coupled with the state and cognitive functions of the brain. However, it is still debated whether the oscillations of brain activity actually carry information. Here, we briefly introduce the biological findings of brain oscillations, and summarize the recent progress in understanding how oscillations mediate brain function. Finally, we examine the possible relationship between brain cognitive function and oscillation, focusing on how oscillation is related to memory, particularly with respect to state-dependent memory formation and memory retrieval under specific brain waves. We propose that oscillatory waves in the neocortex contribute to the synchronization and activation of specific memory trace ensembles in the neocortex by promoting long-range neural communication.

scholarly journals Therapeutic Use of the Ketogenic Diet in Refractory Epilepsy: What We Know and What Still Needs to Be Learned

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2616
Author(s):  
Iwona Maria Zarnowska
Keyword(s):  
Ketogenic Diet ◽  
Refractory Epilepsy ◽  
Epileptic Activity ◽  
Clinical Settings ◽  
Modified Atkins Diet ◽  
Current State ◽  
Wide Range ◽  
Low Carbohydrate ◽  
The Brain

Ketogenic diet (KD) has been used to treat epilepsy for 100 years. It is a high-fat, low-carbohydrate, and sufficient-protein-for-growth diet that mimics the metabolic changes occurring during starvation. Except for classic KD, its modified counterparts, including modified Atkins diet and low-glycemic-index treatment, have gained grounds to increase palatability and adherence. Strong evidence exists that the KD offers protection against seizures in difficult-to-treat epilepsy and possesses long-lasting anti-epileptic activity, improving long-term disease outcome. The KD can also provide symptomatic and disease-modifying activity in a wide range of neurodegenerative diseases. In an era of highly available new anti-seizure medications (ASMs), the challenge of refractory epilepsy has still not been solved. This metabolic therapy is increasingly considered due to unique mechanisms and turns out to be a powerful tool in the hands of a skillful team. Despite decades of extensive research to explain the mechanism of its efficacy, the precise mechanism of action is to date still largely unknown. The key feature of this successful diet is the fact that energy is derived largely from fat but not from carbohydrates. Consequently, fundamental change occurs regarding the method of energy production that causes alterations in numerous biochemical pathways, thus restoring energetic and metabolic homeostasis of the brain. There are barriers during the use of this special and individualized therapy in many clinical settings worldwide. The aim of this review is to revisit the current state of the art of therapeutic application of KD in refractory epilepsy.

Brain-Computer Interfaces and Visual Activity

2013 ◽  
pp. 1549-1570
Author(s):  
Carmen Vidaurre ◽  
Andrea Kübler ◽  
Michael Tangermann ◽  
Klaus-Robert Müller ◽  
José del R. Millán
Keyword(s):  
Video Game ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Robot Arm ◽  
Virtual Keyboard ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Brain States ◽  
Physically Disabled People ◽  
The Brain

There is growing interest in the use of brain signals for communication and operation of devices, in particular, for physically disabled people. Brain states can be detected and translated into actions such as selecting a letter from a virtual keyboard, playing a video game, or moving a robot arm. This chapter presents what is known about the effects of visual stimuli on brain activity and introduces means of monitoring brain activity. Possibilities of brain-controlled interfaces, either with the brain signals as the sole input or in combination with the measured point of gaze, are discussed.

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