scholarly journals Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Narusci S. Bastos ◽  
Bianca P. Marques ◽  
Diana F. Adamatti ◽  
Cleo Z. Billa
Keyword(s):  
Decision Trees ◽  
Visually Impaired ◽  
Eeg Signals ◽  
Beta Band ◽  
Brain Signals ◽  
Visually Impaired People ◽  
Machine Interface ◽  
Sense Of Touch ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.

scholarly journals Security Solutions Using Brain Signals

2018 ◽  
Vol 7 (2) ◽  
pp. 105
Author(s):  
Anupama. H.S ◽  
Anusha M ◽  
Aparna Joshi ◽  
Apoorva N ◽  
N.K. Cauvery ◽  
...  
Keyword(s):  
Current Model ◽  
Neural Interface ◽  
Brain Machine Interface ◽  
Eeg Signals ◽  
Brain Signals ◽  
Thought Pattern ◽  
Machine Interface ◽  
Image Signature ◽  
The Brain ◽  
Communication Path

A Brain Computer Interface is a direct neural interface or a brain–machine interface. It provides a communication path between human brain and the computer system. It aims to convey people's intentions to the outside world directly from their thoughts. This paper focuses on current model which uses brain signals for the authentication of users. The Electro- Encephalogram (EEG) signals are recorded from the neuroheadset when a user is shown a key image (signature image). These signals are further processed and are interpreted to obtain the thought pattern of the user to match them to the stored password in the system. Even if other person is presented with the same key image it fails to authenticate as the cortical folds of the brain are unique to each human being just like a fingerprint or DNA.

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.

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.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

scholarly journals A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results

2019 ◽  
Vol 63 (3) ◽  
pp. 425-434 ◽  
Author(s):  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
3D Video ◽  
Support Vector ◽  
Least Squares Regression ◽  
Eeg Signals ◽  
3D Technology ◽  
Svm Classification ◽  
Brain Signals ◽  
Short Period ◽  
The Impact ◽  
The Brain

Abstract Despite the development of two- and three-dimensional (2D&3D) technology, it has attracted the attention of researchers in recent years. This research is done to reveal the detailed effects of 2D in comparison with 3D technology on the human brain waves. The impact of 2D&3D video watching using electroencephalography (EEG) brain signals is studied. A group of eight healthy volunteers with the average age of 31 ± 3.06 years old participated in this three-stage test. EEG signal recording consisted of three stages: After a bit of relaxation (a), a 2D video was displayed (b), the recording of the signal continued for a short period of time as rest (c), and finally the trial ended. Exactly the same steps were repeated for the 3D video. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. After testing all the EEG frequency bands, delta and theta were extracted as the features. Partial least squares regression (PLSR) and Support vector machine (SVM) classification algorithms were considered in order to classify EEG signals obtained as the result of 2D&3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions.

scholarly journals Co-designing with visually impaired people in the museum

2021 ◽  
pp. 53-64
Author(s):  
Eveline Almeida
Keyword(s):  
Comparative Study ◽  
Visually Impaired ◽  
Design Approach ◽  
Comparative Case Study ◽  
Future Studies ◽  
Visually Impaired People ◽  
Museum Object ◽  
Impaired People ◽  
Design Projects

This article aims to conduct an analysis of the use of co-design methodologies in accessibility projects for the visually impaired in museums. The article presents a discussion of the practices of participatory methodologies in museum projects, in particular the co-design approach. It proceeds with a comparative case study, analyzing articles that describe the implementation of co-design projects in museum and educational contexts. The purpose of this comparative study is to present different methods of co-design, what goals can be achieved by these methodologies and to analyze and compare the results and challenges found. The authors selected four co-design projects for this comparative study, analyzing their differences and similarities. The conclusion of this analysis is that participatory projects bring as a benefit not only the creation of new audiences, but the enrichment of the museum object itself, promoting new forms of enjoyment and acquisition of knowledge. This article aims to contribute to the development of future studies and projects on accessibility that may represent better

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.

Sign in / Sign up

Export Citation Format

Share Document