Extracting Human Brain Signals from the EEG Records Using LabVIEW and Advanced Signal Processing

2022 ◽  
pp. 183-190
Author(s):  
Andrei Medvedev ◽  
Valentina Temkina ◽  
Armen Makaryan ◽  
Eduard Sivolenko ◽  
Babken Hovhannisyan ◽  
...  
Keyword(s):  
Signal Processing ◽  
Human Brain ◽  
Brain Signals

scholarly journals Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Manuel J. A. Eugster ◽  
Tuukka Ruotsalo ◽  
Michiel M. Spapé ◽  
Oswald Barral ◽  
Niklas Ravaja ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Signals

Social Robots for Pedagogical Rehabilitation

2019 ◽  
pp. 1-21 ◽  
Author(s):  
Vassilis G. Kaburlasos ◽  
Eleni Vrochidou
Keyword(s):  
Special Education ◽  
Human Brain ◽  
Learning Difficulties ◽  
Social Robots ◽  
Limited Capacity ◽  
Learning Tools ◽  
Pilot Studies ◽  
Brain Signals ◽  
Preliminary Work ◽  
Preexisting Conditions

The use of robots as educational learning tools is quite extensive worldwide, yet it is rather limited in special education. In particular, the use of robots in the field of special education is under skepticism since robots are frequently believed to be expensive with limited capacity. The latter may change with the advent of social robots, which can be used in special education as affordable tools for delivering sophisticated stimuli to children with learning difficulties also due to preexisting conditions. Pilot studies occasionally demonstrate the effectiveness of social robots in specific domains. This chapter overviews the engagement of social robots in special education including the authors' preliminary work in this field; moreover, it discusses their proposal for potential future extensions involving more autonomous (i.e., intelligent) social robots as well as feedback from human brain signals.

scholarly journals Identity Recognition Using Biological Electroencephalogram Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Liang ◽  
Liang Cheng ◽  
Mingdong Tang
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Human Brain ◽  
State Of The Art ◽  
Brain Wave ◽  
Identity Recognition ◽  
Wave Signal ◽  
Machine Learning Methods ◽  
Characteristic Extraction ◽  
Critical Issues

Brain wave signal is a bioelectric phenomenon reflecting activities in human brain. In this paper, we firstly introduce brain wave-based identity recognition techniques and the state-of-the-art work. We then analyze important features of brain wave and present challenges confronted by its applications. Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain wave signal processing. Afterwards, we survey the critical issues of characteristic extraction, classification, and selection involved in brain wave signal processing. Finally, we propose several brain wave-based identity recognition techniques for further studies and conclude this paper.

scholarly journals Single-dimensional human brain signals for two-dimensional economic choice options

2021 ◽  
pp. JN-RM-1555-20
Author(s):  
Leo Chi U Seak ◽  
Konstantin Volkmann ◽  
Alexandre Pastor-Bernier ◽  
Fabian Grabenhorst ◽  
Wolfram Schultz
Keyword(s):  
Human Brain ◽  
Two Dimensional ◽  
Economic Choice ◽  
Brain Signals

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

scholarly journals Accelerated Robot Learning via Human Brain Signals

2020 ◽  
Author(s):  
Iretiayo Akinola ◽  
Zizhao Wang ◽  
Junyao Shi ◽  
Xiaomin He ◽  
Pawan Lapborisuth ◽  
...  
Keyword(s):  
Human Brain ◽  
Robot Learning ◽  
Brain Signals

Toward a Bioinspired Fusion of Color and Infrared Textural Image Information

2001 ◽  
Vol 5 (6) ◽  
pp. 326-332
Author(s):  
Javier Ruiz-del-Solar ◽  
◽  
Aureli Soria-Frisch ◽  
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Human Brain ◽  
Information Fusion ◽  
Visual Information ◽  
Processing Technologies ◽  
Pattern Recognition Systems ◽  
Recognition Systems ◽  
Organizational Principles ◽  
Processing Of Visual Information

Simultaneous progress in sensor and signal processing technologies stimulates the implementation of more refined pattern recognition systems in order to solve problems of increasing complexity. The progress on both technologies induced the implementation of the here presented framework for the fusion of infrared and color textural information. The framework is based on different aspects of the processing of visual information in the human brain. Some organizational principles of multisensorial information fusion in higher associative areas are also reflected in it. Preliminary results, realized in a simplified framework, show the validity of the biological-based approach in the resolution of multisensorial image fusion.

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