Exocortex as a Learning Technology

Computer Interface ◽

Management Approach ◽

Learning Technology ◽

Research Projects ◽

Lifelong Learners ◽

Computational Environment ◽

Brain Interface ◽

Exocortex is a hypothetical technology where the human brain can connect to a brain implant or a computational environment which is in the state of a wearable device, using two-way brain-computer interface, in order to augment the cognitive powers of the human brain such as perception, storage, recollection and processing. Exocortex is expected to be a part of everyday life in the 2030s. Exocortex technology is supported by parallel technologies such as brain reading, uploading knowledge into the brain from the outside, brain-computer interface, brain-to-brain interface, which are now undergoing prototype applications. In this study, by discussing the potential of exocortex technology in its use for learning processes, as a result of handling it with the “learning experiences management” approach, the opportunities it provides specifically for lifelong learners are examined. In the results and recommendations section of the study, a foresight is given for the scientific research projects that can be performed for this purpose.

Analysis of Performance Metrics Using Emotiv EPOC+

MATEC Web of Conferences ◽

10.1051/matecconf/201821004046 ◽

2018 ◽

Vol 210 ◽

pp. 04046 ◽

Cited By ~ 2

Author(s):

Martin Strmiska ◽

Zuzana Koudelkova

Keyword(s):

Human Brain ◽

Performance Metrics ◽

Brain Activity ◽

Real Life ◽

Computer Interface ◽

Medical Field ◽

Precise Tool ◽

Analysis Of Performance ◽

Brain-computer interface (BCI) is a device that enables the connection between the human brain and a computer, therefore, it allows us to observe the brain activity. The goal of this article is to prove that brain-computer interface is a helpful and quite precise tool. This goal will be achieved by presenting various examples from real-life situations. The results show that this device is indeed helpful, e.g. in a medical field, however, it is not commonly used in hospitals.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

2019 ◽

pp. 139-145

Author(s):

Selma Büyükgöze

Keyword(s):

Computer Interface ◽

Electrode Placement ◽

Eeg Signals ◽

Non Invasive ◽

Brain Signals ◽

Brain States ◽

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.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

Methods of Information in Medicine ◽

10.1055/s-0038-1634391 ◽

2002 ◽

Vol 41 (04) ◽

pp. 337-341 ◽

Cited By ~ 24

Author(s):

F. Cincotti ◽

D. Mattia ◽

C. Babiloni ◽

F. Carducci ◽

L. Bianchi ◽

...

Keyword(s):

Mahalanobis Distance ◽

Computer Interface ◽

Correct Classification ◽

Interface Area ◽

System A ◽

The Brain ◽

Full Covariance Matrix ◽

Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface

PLoS ONE ◽

10.1371/journal.pone.0206107 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0206107 ◽

Cited By ~ 8

Author(s):

Sebastian Nagel ◽

Martin Spüler

Keyword(s):

High Speed ◽

Visual Stimulation ◽

Computer Interface ◽

Brain Response ◽

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.310.660 ◽

2013 ◽

Vol 310 ◽

pp. 660-664 ◽

Cited By ~ 1

Author(s):

Zi Guang Li ◽

Guo Zhong Liu

Keyword(s):

Communication Channel ◽

Computer Interface ◽

Auditory Stimuli ◽

Support Vector ◽

Quality Life ◽

Asking Questions ◽

Command Signals ◽

Different Characteristics ◽

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers

Anais da Academia Brasileira de Ciências ◽

10.1590/0001-3765201520130413 ◽

2015 ◽

Vol 87 (4) ◽

pp. 1929-1937 ◽

Cited By ~ 3

Author(s):

Regina O. Heidrich ◽

Emely Jensen ◽

Francisco Rebelo ◽

Tiago Oliveira

Keyword(s):

Cerebral Palsy ◽

Comparative Study ◽

Case Studies ◽

Product Evaluation ◽

Computer Interface ◽

Healthy Controls ◽

ABSTRACT This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).