scholarly journals Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Leeb ◽  
Doron Friedman ◽  
Gernot R. Müller-Putz ◽  
Reinhold Scherer ◽  
Mel Slater ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Beta Oscillations ◽  
Brain Waves ◽  
The Subject ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Spinal Cord Injured

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

scholarly journals Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

2020 ◽  
Author(s):  
Xiao Zhang ◽  
Dongrui Wu ◽  
Lieyun Ding ◽  
Hanbin Luo ◽  
Chin-Teng Lin ◽  
...  
Keyword(s):  
Visual Evoked Potential ◽  
Brain Computer Interface ◽  
Effective Means ◽  
Computer Interface ◽  
Eeg Signals ◽  
Input Text ◽  
User Frustration ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Lateral Sclerosis

Abstract An electroencephalogram (EEG)-based brain–computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

scholarly journals Online Detection of P300 and Error Potentials in a BCI Speller

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Bernardo Dal Seno ◽  
Matteo Matteucci ◽  
Luca Mainardi
Keyword(s):  
Genetic Algorithm ◽  
Error Correction ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Preliminary Results ◽  
On Line ◽  
The Subject ◽  
First Time ◽  
Automatic Error ◽  
Correction System

Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances.

Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and brain-computer interface system: a case study

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Natalia Browarska ◽  
Aleksandra Kawala-Sterniuk ◽  
Przemysław Chechelski ◽  
Jarosław Zygarlicki
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Initial Study ◽  
Computer Interface ◽  
Expanded Group ◽  
Brain Waves ◽  
System A ◽  
Eeg Data ◽  
Interface System

AbstractObjectivesThis presents a case for fear and stress stimuli and afterward EEG data analysis.MethodsThe stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation.ResultsResults obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants.ConclusionsThe study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

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.

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.

Geons and Non-Accidental Relations in 2D Shape Abstraction: A BCI Study

2014 ◽  
Author(s):  
Akshay Deshpande ◽  
Ehsan T. Esfahani ◽  
Rahul Rai
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Shape Recognition ◽  
Effective Means ◽  
Line Drawing ◽  
Computer Interface ◽  
Line Drawings ◽  
Human Understanding ◽  
Electroencephalogram Eeg ◽  
Shape Abstraction

Simple line drawings and 2D sketches are commonly used by humans to convey their ideas about a particular shape or shapes in an image. These approximations of shapes are effective means for visual communication and artistic practices. The idea of shape abstraction can be derived from such approximations of shapes, which considers their most important and salient features. The key idea behind shape abstraction is to extract a simplified version of a shape that preserves the salient characteristics of the input shape. In this paper, we introduce and analyze a slightly different and novel facet of abstraction, which we call “partial to full shape recognition” of two dimensional shapes (line drawing and sketches). The key idea is recognizing partial 2D shapes that leads to recognition of full shape utilizing the theory of recognition-by-components (RBC) and geons (human shape perception). We segment the 2D shapes according to the non-accidental relations provided by RBC and analyze the electroencephalogram (EEG) brain activity of subjects using a brain computer interface (BCI) to gain knowledge of human understanding of such relations pertaining to specific partial to full shape correspondence.

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