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.

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 The Self-Paced Graz Brain-Computer Interface: Methods and Applications

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Reinhold Scherer ◽  
Alois Schloegl ◽  
Felix Lee ◽  
Horst Bischof ◽  
Janez Janša ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Game ◽  
Google Earth ◽  
The Self ◽  
Computer Interface ◽  
Emg Activity ◽  
Intentional Control ◽  
Electroencephalogram Eeg ◽  
Control State

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

scholarly journals BRAIN COMPUTER INTERFACE APPLICATION IN STROKE DISEASE DIAGNOSIS

2016 ◽  
Vol 4 (7) ◽  
pp. 94-101
Author(s):  
Igwe J. S. ◽  
Inyiama ◽  
OgbuNwani Henry
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Research Work ◽  
Disease Diagnosis ◽  
Computer Interface ◽  
Theta Wave ◽  
Brain Signals ◽  
The Status ◽  
Electroencephalogram Eeg ◽  
The Brain

Every discovery is geared towards problem solving. This is manifested by the advent of brain computer interface (BCI). Brain computer interface (BCI) is a field of study concern with the detection and utilization of brain signals in establishing the communication path between the brain and the computer system. The knowledge of this science has helped in no small measure in providing solutions to several challenges befalling man and his environment. In this paper, we explored those areas where BCI has proved useful and pointed out as well its possible application in diagnosis of stroke disease. The discourse was centered on detection of electrochemical signals from the brain called electroencephalogram (EEG). The research work also highlighted the technique of recording brain activity via electroencephalogram and using it in making deduction on the status of stroke attack on individual. This can either be normal or abnormal. The presence of delta or theta wave in an awaked adult suggests an abnormal situation. While the observance of alpha, beta and gamma waves are interpreted as normal.

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.

scholarly journals Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

2021 ◽  
Author(s):  
Yiwen Wang ◽  
Yuxiao Lin ◽  
Chao Fu ◽  
Zhihua Huang ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural Mechanisms ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Signals ◽  
Chicken Game ◽  
Common Response ◽  
Tit For Tat ◽  
Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

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 Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

2019 ◽  
Author(s):  
Jennifer Stiso ◽  
Marie-Constance Corsi ◽  
Javier Omar Garcia ◽  
Jean M Vettel ◽  
Fabrizio De Vico Fallani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Distributed Networks ◽  
Motor Dysfunction ◽  
Computer Interface ◽  
Specific Method ◽  
Temporal Expression ◽  
And Behavior ◽  
Brain Behavior

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

scholarly journals Could Brain–Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?

2021 ◽  
Vol 15 ◽  
Author(s):  
Stuti Chakraborty ◽  
Gianluca Saetta ◽  
Colin Simon ◽  
Bigna Lenggenhager ◽  
Kathy Ruddy
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Treatment Options ◽  
Body Representation ◽  
Vestibular Stimulation ◽  
Current Treatment ◽  
Substantial Improvement ◽  
Computer Interface ◽  
Effective Form ◽  
Brain Correlates

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person’s anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain–computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

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.

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