scholarly journals The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Romain Grandchamp ◽  
Arnaud Delorme
Keyword(s):  
Scientific Exploration ◽  
Technological Advances ◽  
Eeg Data ◽  
Machine Interface ◽  
Entertainment Value ◽  
User Friendly ◽  
Electroencephalogram Eeg ◽  
Subjective Learning ◽  
Neuroimaging Techniques ◽  
New Framework

Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies. The system has been demonstrated through several performances involving an interaction between the subject controlling the BMI, a musician, and the audience during series of exhibitions and workshops in schools. We report here feedback from 134 participants who filled questionnaires to rate their experiences. Our results show improved subjective learning compared to conventional methods, improved entertainment value, improved absorption into the material being presented, and little discomfort.

Reduced electroencephalogram complexity in postoperative delirium

2021 ◽  
Author(s):  
Sean Tanabe ◽  
Maggie Parker ◽  
Richard Lennertz ◽  
Robert A Pearce ◽  
Matthew I Banks ◽  
...  
Keyword(s):  
Postoperative Delirium ◽  
Primary Outcome ◽  
Rating Scale ◽  
Monocyte Chemoattractant Protein 1 ◽  
Monocyte Chemoattractant ◽  
Monocyte Chemoattractant Protein ◽  
Eeg Data ◽  
Before And After ◽  
Electroencephalogram Eeg ◽  
Multiple Comparison Correction

Abstract Delirium is associated with electroencephalogram (EEG) slowing and impairments in connectivity. We hypothesized that delirium would be accompanied by a reduction in the available cortical information (i.e. there is less information processing occurring), as measured by a surrogate, Lempil-Ziv Complexity (LZC), a measure of time-domain complexity. Two ongoing perioperative cohort studies (NCT03124303, NCT02926417) contributed EEG data from 91 patients before and after surgery; 89 participants were used in the analyses. After cleaning and filtering (0.1-50Hz), the perioperative change in LZC and LZC normalized (LZCn) to a phase-shuffled distribution were calculated. The primary outcome was the correlation of within-patient paired changes in delirium severity (Delirium Rating Scale-98 [DRS]) and LZC. Scalp-wide threshold free cluster enhancement was employed for multiple comparison correction. LZC negatively correlated with DRS in a scalp-wide manner (peak channel r 2=0.199, p<0.001). This whole brain effect remained for LZCn, though the correlations were weaker (peak channel r 2=0.076, p=0.010). Delirium diagnosis was similarly associated with decreases in LZC (peak channel p<0.001). For LZCn, the topological significance was constrained to the midline posterior regions (peak channel p=0.006). We found a negative correlation of LZC in the posterior and temporal regions with monocyte chemoattractant protein-1 (peak channel r 2=0.264, p<0.001, n=47) but not for LZCn. Complexity of the EEG signal fades proportionately to delirium severity implying reduced cortical information. Peripheral inflammation, as assessed by monocyte chemoattractant protein-1, does not entirely account for this effect, suggesting that additional pathogenic mechanisms are involved.

scholarly journals Implant Surgery Using Bio-compatible Guides

2018 ◽  
Vol 55 (1) ◽  
pp. 38-41
Author(s):  
Serban Talpos ◽  
Tareq Hajaj ◽  
Costin Timofte ◽  
Mircea Rivis ◽  
Felicia Streian ◽  
...  
Keyword(s):  
Primary Stability ◽  
Daily Practice ◽  
Implant Placement ◽  
Surgical Guides ◽  
Technological Advances ◽  
Tissue Healing ◽  
The Future ◽  
Oral Tissue ◽  
The One ◽  
User Friendly

Implants and biomaterials used in hard and soft oral tissue augmentation are very complex, but predictable to use nowadays, as the technological advances haven�t skipped this field of medicine. Cases that were impossible to treat with implant retained fixed prosthesis some years ago, have become the daily practice of oral surgeons and dentists around the world. The new user-friendly products, together with simplified protocols, increased the practitioners� predictability and success rate, thus the biomaterial industry took a huge leap forward. As the biomaterial industry keeps developing continuously, making better and safer products, the surgical and prosthetic protocols evolve and change as well. On this matter, the implant placement has become safer, using digital surgical guides. Guided implant placement doesn�t just allow the practitioner place the implant in the patient�s bone, but, moreover, it helps him place it in the correct, 3D, prosthetic position. And, thus, guiding the future bone augmentation and regeneration as well, accordingly. So, the implant placement has shifted from bone-orientated to prosthetic-orientated, offering at the same time a better primary stability for the implants, due to the prior planning. The present clinical study aims to analyze the outcome of the digital guided protocol. Unlike the free-handed surgery, the digital guided surgery allows dentists and oral surgeons to place implants according to the future prosthetic position of the crowns, even in conditions of alveolar ridges with bone resorption. Moreover, it makes possible the �one day implant� concept, the dental technician being able to create the provisional crown/s in advance, knowing precisely the future position of the implant placement. So, at the time of the surgery, the provisional crown is also put in place, guiding the soft and hard tissue healing and also giving the patient a greater satisfaction.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Towards the Real-Life EEG Applications

2019 ◽  
pp. 305-317
Author(s):  
Guangyi Ai
Keyword(s):  
Data Analysis ◽  
Real Life ◽  
The Real ◽  
Life Condition ◽  
Research Fields ◽  
Eeg Data ◽  
Artifact Suppression ◽  
Electroencephalogram Eeg ◽  
Real Life Condition ◽  
Brain Monitoring

Electroencephalogram (EEG) is one of the most popular approaches for brain monitoring in many research fields. While the detailed working flows for in-lab neuroscience-targeted EEG experiments conditions have been well established, carrying out EEG experiments under a real-life condition can be quite confusing because of various practical limitations. This chapter gives a brief overview of the practical issues and techniques that help real-life EEG experiments come into being, and the well-known artifact problems for EEG. As a guideline for performing a successful EEG data analysis with the low-electrode-density limitation of portable EEG devices, recently proposed techniques for artifact suppression or removal are briefly surveyed as well.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

EEG Data Mining Using PCA

2011 ◽  
pp. 161-180 ◽  
Author(s):  
Lenka Lhotská ◽  
Vladimír Krajca ◽  
Jitka Mohylová ◽  
Svojmil Petránek ◽  
Václav Gerla
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Principal Components ◽  
The Neural Network ◽  
Eeg Data ◽  
Eigen Decomposition ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike ◽  
Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition

2019 ◽  
Vol 31 (5) ◽  
pp. 919-942 ◽  
Author(s):  
Xian-Lun Tang ◽  
Wei-Chang Ma ◽  
De-Song Kong ◽  
Wei Li
Keyword(s):  
Supervised Learning ◽  
Motor Imagery ◽  
Adaptive Learning ◽  
Recognition Accuracy ◽  
Learning Rate ◽  
Data Sets ◽  
Adaptive Learning Rate ◽  
Eeg Data ◽  
Contrastive Divergence ◽  
Electroencephalogram Eeg

Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.

scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

Experimental Identification of Model Parameters and the Statistical Processing Using a Nonlinear Oscillator Applied to EEG Analysis

2018 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Stress State ◽  
Nonlinear Oscillator ◽  
Statistical Processing ◽  
Psychological State ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Model ◽  
Eeg Data ◽  
Machine Interface ◽  
Almost All

The nonlinear analysis may help to reveal the complex behavior of the Electroencephalogram (EEG) signal. In order to analyze the EEG in real time, we have proposed an EEG analysis model using a nonlinear oscillator with one degree of freedom and minimum required parameters. Our method identifies EEG model parameters experimentally. The purpose of this study is to examine the specific characteristic of model parameters. Validation of the method and investigation of characteristic of model parameters were conducted based on alpha frequency EEG data in both relax state and stress state. The results of the parameter identification with the time sliding window for 1 second show almost all of the identified parameters have a normal distribution spread around the average. The model outputs can closely match the complicated experimental EEG data. The results also showed that the existence of nonlinear term in the EEG analysis is crucial and the linearity parameter shows a certain tendency as the nonlinearity increases. Furthermore, the activities of EEG become linear on the mathematical model when suddenly change from the relax state to the stress state. The results indicate that our method may provide useful information in various field including the quantification of human mental or psychological state, diagnosis of brain disease such as epilepsy and design of brain machine interface.

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