scholarly journals Electrocortical temporal complexity during wakefulness and sleep: an updated account

2020 ◽  
Author(s):  
Joaquín González ◽  
Matias Cavelli ◽  
Alejandra Mondino ◽  
Claudia Pascovich ◽  
Santiago Castro-Zaballa ◽  
...  
Keyword(s):  
Electrode Configuration ◽  
Behavioral State ◽  
Complex Activity ◽  
Temporal Complexity ◽  
Physiological Processes ◽  
Brain Signals ◽  
Controversial Topic ◽  
The Brain

AbstractThe states of sleep and wakefulness are critical physiological processes associated with different brain patterns of activity. The intracranial electroencephalogram allows us to measure these changes, thus, it is a critical tool for its study. Recently, we showed that the electrocortical temporal complexity decreased from wakefulness to sleep. Nevertheless, the origin of this complex activity remains a controversial topic due to the existence of possible artifacts contaminating the brain signals. In this work, we showed that complexity decreases during sleep, independently of the electrode configuration employed. This fact strongly suggests that the basis for the behavioral-state differences in complexity does not have an extracranial origin; i.e., generated from the brain.

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.

Differentiation of Healthy Individuals from Those with Autism Spectrum Disorders using Information Graph of Complementary Opposites

2020 ◽  
Keyword(s):  
Children With Autism ◽  
Autism Spectrum ◽  
Brain Signals ◽  
Three Stages ◽  
The Mean ◽  
Spectrum Disorders ◽  
Pediatric Psychiatry ◽  
The Brain ◽  
Optimal Feature ◽  
Information Graph

This study aimed to examine the brain signals of children with Autism Spectrum Disorder (ASD) and use a method according to the concept of complementary opposites to obtain the prominent features or a pattern of EEG signal that represents the biological characteristic of such children. In this study, 20 children with the mean±SD age of 8±5 years were divided into two groups of normal control (NC) and ASD. The diagnosis and approval of individuals in both groups were conducted by two experts in the field of pediatric psychiatry and neurology. The recording protocol was designed with the most accuracy; therefore, the brain signals were recorded with the least noise in the awake state of the individuals in both groups. Moreover, the recording was conducted in three stages from two channels (C3-C4) of EEG ( referred to as the central part of the brain) which were symmetrical in function. In this study, the Mandala method was adopted based on the concept of complementary opposites to investigate the features extracted from Mandala pattern topology and obtain new features and pseudo-patterns for the screening and early diagnosis of ASD. The optimal feature here was based on different stages of processing and statistical analysis of Pattern Detection Capability (PDC). The PDC is a biomarker derived from the Mandala pattern for differentiating the NC from ASD groups.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals EEG and Sonic Platforms to Enhance Mindfulness Meditation

2016 ◽  
Vol 5 (9) ◽  
pp. 1
Author(s):  
Caitilin De Berigny ◽  
Freya Zinovieff ◽  
Karen Cochrane ◽  
Youngdong Kim ◽  
Zhepeng Rui
Keyword(s):  
Clinical Practice ◽  
Mindfulness Meditation ◽  
Interactive Design ◽  
Historical Background ◽  
Technology Research ◽  
Interactive Installation ◽  
Interactive Applications ◽  
Physiological Processes ◽  
Electroencephalogram Eeg ◽  
The Brain

<p>This paper explores interactive applications that encourage mindfulness through sensors and novel input technology. Research in psychology and neuroscience demonstrating the benefits of mindfulness is initiating a new movement in interactive design. As cutting edge technologies become more accessible they are being employed to research and explore the practice of mindfulness. We examine three interactive installation artworks that promote mindfulness. In order to contextualize the interactive artworks discussed we first examine the historical background of the Electroencephalogram (EEG). We then discuss the physiological processes of meditation and the history behind the clinical practice of mindfulness. We show how artists and designers employ EEG sensors, to record the electrical activity of the brain to visualize mindfulness meditation practices. Lastly, we conclude the paper by discussing the future of the three artworks.</p>

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 BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

2018 ◽  
Author(s):  
Linxing Jiang ◽  
Andrea Stocco ◽  
Darby M. Losey ◽  
Justin A. Abernethy ◽  
Chantel S. Prat ◽  
...  
Keyword(s):  
Problem Solving ◽  
Magnetic Stimulation ◽  
Human Subjects ◽  
Occipital Cortex ◽  
Collaborative Problem Solving ◽  
Incorrect Decision ◽  
Brain Signals ◽  
Noisy Signals ◽  
Brain Interface ◽  
The Brain

ABSTRACTWe present BrainNet which, to our knowledge, is the first multi-human non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.

scholarly journals Complex Activity and Short-term Memories in Reciprocally Connected Cerebral Organoids

2021 ◽  
Author(s):  
Tatsuya Osaki ◽  
Yoshiho Ikeuchi
Keyword(s):  
Human Brain ◽  
Three Dimensional ◽  
Oscillatory Activity ◽  
Cellular Functions ◽  
Complex Activity ◽  
Axonal Connections ◽  
Brain Organoid ◽  
External Stimuli ◽  
The Brain

AbstractMacroscopic axonal connections in the human brain distribute information and neuronal activity across the brain. Although this complexity previously hindered elucidation of functional connectivity mechanisms, brain organoid technologies have recently provided novel avenues to investigate human brain function by constructing small segments of the brain in vitro. Here, we describe the neural activity of human cerebral organoids reciprocally connected by a bundle of axons. Compared to conventional organoids, connected organoids produced significantly more intense and complex oscillatory activity. Optogenetic manipulations revealed that the connected organoids could re-play and recapitulate over time temporal patterns found in external stimuli, indicating that the connected organoids were able to form and retain temporal memories. Our findings suggest that connected organoids may serve as powerful tools for investigating the roles of macroscopic circuits in the human brain – allowing researchers to dissect cellular functions in three-dimensional in vitro nervous system models in unprecedented ways.

scholarly journals Using Connectivity to Explain Autism

2019 ◽  
Author(s):  
Jack Adamek ◽  
Yu Luo ◽  
Joshua Ewen
Keyword(s):  
Scientific Explanation ◽  
Causal Explanation ◽  
Autism Spectrum ◽  
Imaging Method ◽  
Imaging Data ◽  
Reverse Engineer ◽  
Physiological Processes ◽  
The Mind ◽  
The Brain

The chapters in this Handbook reveal the breadth of brilliant imaging and analysis techniques designed to fulfill the mandate of cognitive neuroscience: to understand how anatomical structures and physiological processes in the brain cause typical and atypical behavior. Yet merely producing data from the latest imaging method is insufficient to truly achieve this goal. We also need a mental toolbox that contains methods of inference that allow us to derive true scientific explanation from these data. Causal inference is not easy in the human brain, where we are limited primarily to observational data and our methods of experimental perturbation in the service of causal explanation are limited. As a case study, we reverse engineer one of the most influential accounts of a neuropsychiatric disorder that is derived from observational imaging data: the connectivity theories of autism spectrum disorder (ASD). We take readers through an approach of first considering all possible causal paths that are allowed by preliminary imaging-behavioral correlations. By progressively sharpening the specificity of the measures and brain/behavioral constructs, we iteratively chip away at this space of allowable causal paths, like the sculptor chipping away the excess marble to reveal the statue. To assist in this process, we consider how current imaging methods that are lumped together under the rubric of “connectivity” may actually offer a differentiated set of connectivity constructs that can more specifically relate notions of information transmission in the mind to the physiology of the brain.

scholarly journals Neocortical microdissection at columnar and laminar resolution for molecular interrogation

2017 ◽  
Author(s):  
Koen Kole ◽  
Tansu Celikel
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
Molecular Mechanisms ◽  
Barrel Cortex ◽  
Brain Slices ◽  
High Quality ◽  
Living Slices ◽  
Physiological Processes ◽  
Molecular Studies ◽  
The Brain

AbstractThe heterogeneous organization of the mammalian neocortex poses a challenge to elucidate the molecular mechanisms underlying its physiological processes. Although high-throughput molecular methods are increasingly deployed in neuroscience, their anatomical specificity is often lacking. Here we introduce a targeted microdissection technique that enables extraction of high-quality RNA and proteins at high anatomical resolution from acutely prepared brain slices. We exemplify its utility by isolating single cortical columns and laminae from the mouse primary somatosensory (barrel) cortex. Tissues can be isolated from living slices in minutes, and the extracted RNA and protein are of sufficient quantity and quality to be used for RNA-sequencing and mass spectrometry. This technique will help to increase the anatomical specificity of molecular studies of the neocortex, and the brain in general as it is applicable to any brain structure that can be identified using optical landmarks in living slices.

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