scholarly journals Towards the Recognition of the Emotions of People with Visual Disabilities through Brain–Computer Interfaces

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2620
Author(s):  
Jesús Leonardo López-Hernández ◽  
Israel González-Carrasco ◽  
José Luis López-Cuadrado ◽  
Belén Ruiz-Mezcua
Keyword(s):  
Affective Computing ◽  
Emotional State ◽  
Brain Computer Interface ◽  
The Other ◽  
Computer Interface ◽  
Visual Disabilities ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Use Of Technology ◽  
Different Types

A brain–computer interface is an alternative for communication between people and computers, through the acquisition and analysis of brain signals. Research related to this field has focused on serving people with different types of motor, visual or auditory disabilities. On the other hand, affective computing studies and extracts information about the emotional state of a person in certain situations, an important aspect for the interaction between people and the computer. In particular, this manuscript considers people with visual disabilities and their need for personalized systems that prioritize their disability and the degree that affects them. In this article, a review of the state of the techniques is presented, where the importance of the study of the emotions of people with visual disabilities, and the possibility of representing those emotions through a brain–computer interface and affective computing, are discussed. Finally, the authors propose a framework to study and evaluate the possibility of representing and interpreting the emotions of people with visual disabilities for improving their experience with the use of technology and their integration into today’s society.

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

scholarly journals A Survey of Interactive Systems based on Brain-Computer Interfaces

2013 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Luiz Stamatto Ferreira ◽  
Leonardo Cunha de Miranda ◽  
Erica Esteves Cunha de Miranda ◽  
Sarah Gomes Sakamoto
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Research Area ◽  
Interactive Systems ◽  
Computer Interface ◽  
Immersive Environments ◽  
User Interactions ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Near Future

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

The Application of BCI Technology in Android RPG Game

2014 ◽  
Vol 496-500 ◽  
pp. 2015-2018
Author(s):  
Jing Hai Yin ◽  
Zheng Dong Mu ◽  
Jian Feng Hu
Keyword(s):  
Brain Computer Interface ◽  
Human Interaction ◽  
Research Interest ◽  
Computer Interface ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Brain Signals

To enhance human interaction with machines, research interest is growing to develop a Brain-Computer Interface (BCI), which allows communication of a human with a machine only by use of brain signals. In this paper, one type of android RPG game was designed for application of brain computer interfaces.

scholarly journals Improving the Performance of Brain-Computer Interface using Deep Learning Algorithms and Event-Related Spectral Perturbation

2020 ◽  
Vol 8 (6) ◽  
pp. 3756-3763
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Computer Interface ◽  
Neuromuscular Disorders ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Computer Interface ◽  
Brain Signals ◽  
Spectral Perturbation

Brain Computer Interface allows disabled people to communicate with the external world by using their brain signals. The main goal of a BCI is to provide patients who suffer form any neuromuscular disorders whith a communication channel based on their brain signals. In this paper, the aim is to explore the effects of applying deep learning algorithms and Event Related Spectral Perturbation analyses on the performance of different EEG-based BCI paradigms. Two paradigms were investigated: one is based on the Matrix paradigm (known as oddball); and the other one utilizes the Rapid serial visual Presentation (RSVP) for presenting the stimuli. Deep learning algorithms of convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were utilized to evaluate the two paradigms. Our findings showed that Matrix paradigm is more effective in detecting P300 signal. In terms of classification methods, deep learning of CNN algorithm has shown superiority performance in comparison with the other machine learning algorithms.

scholarly journals Classification of Emotions through EEG Signals using SVM and DNN

2020 ◽  
Vol 8 (5) ◽  
pp. 206-209
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Emotional State ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Work Place ◽  
Support Vector ◽  
Eeg Signals ◽  
Brain Signals

Emotions are important for Humans both at work place and in their life. Emotions helps us to communicate with others, to take decisions, in understand others etc., Emotions recognition not only helps us to solve the mental illness but also are important in various application such as Brain Computer Interface , medical care and entertainment This paper mainly deals with how Emotions are Classified through EEG Signals using SVM (Support Vector machine) and DNN (Deep Neural Networks) . Applying the most appropriate algorithm to detect the emotional state of a person and play the corresponding song in the playlist. Brain signals can be collected using EEG (electroencephalography) devices

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.

Feedback Modalities in Brain–Computer Interfaces: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1186-1190
Author(s):  
Wakana Ishihara ◽  
Karen Moxon ◽  
Sheryl Ehrman ◽  
Mark Yarborough ◽  
Tina L. Panontin ◽  
...  
Keyword(s):  
Systematic Review ◽  
Visual Feedback ◽  
Auditory Feedback ◽  
Brain Computer Interface ◽  
Tactile Feedback ◽  
Proprioceptive Feedback ◽  
Computer Interface ◽  
Computer Interfaces ◽  
Feedback Modalities ◽  
Improving Accuracy

This systematic review addresses the plausibility of using novel feedback modalities for brain–computer interface (BCI) and attempts to identify the best feedback modality on the basis of the effectiveness or learning rate. Out of the chosen studies, it was found that 100% of studies tested visual feedback, 31.6% tested auditory feedback, 57.9% tested tactile feedback, and 21.1% tested proprioceptive feedback. Visual feedback was included in every study design because it was intrinsic to the response of the task (e.g. seeing a cursor move). However, when used alone, it was not very effective at improving accuracy or learning. Proprioceptive feedback was most successful at increasing the effectiveness of motor imagery BCI tasks involving neuroprosthetics. The use of auditory and tactile feedback resulted in mixed results. The limitations of this current study and further study recommendations are discussed.

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

scholarly journals Effect of Distracting Background Speech in an Auditory Brain–Computer Interface

2021 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Álvaro Fernández-Rodríguez ◽  
Ricardo Ron-Angevin ◽  
Ernesto J. Sanz-Arigita ◽  
Antoine Parize ◽  
Juliette Esquirol ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Cognitive Workload ◽  
Auditory Event ◽  
Shared Attention ◽  
Attentional Condition ◽  
Different Types ◽  
The Impact ◽  
Auditory Bci

Studies so far have analyzed the effect of distractor stimuli in different types of brain–computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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