scholarly journals A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI

2021 ◽  
Vol 14 ◽  
Author(s):  
Halim I. Baqapuri ◽  
Linda D. Roes ◽  
Mikhail Zvyagintsev ◽  
Souad Ramadan ◽  
Micha Keller ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Dynamic Change ◽  
Self Regulation ◽  
Training Session ◽  
Region Of Interest ◽  
Computer Interface ◽  
Movement Speed ◽  
Technological Advances ◽  
Recent Developments

Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)–based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain–computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player’s avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE’s objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.

scholarly journals Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

2011 ◽  
Vol 6 (3) ◽  
pp. 403 ◽  
Author(s):  
Ioan Dzitac ◽  
Tiberiu Vesselényi ◽  
Radu Cătălin Ţarcă
Keyword(s):  
Fuzzy Inference ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Inference System ◽  
Eeg Data ◽  
Recent Developments ◽  
Potential Activity ◽  
Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

Brain–Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis

2021 ◽  
pp. 154596832110628
Author(s):  
Ippei Nojima ◽  
Hisato Sugata ◽  
Hiroki Takeuchi ◽  
Tatsuya Mima
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Meta Analysis ◽  
Self Regulation ◽  
Large Degree ◽  
Motor Recovery ◽  
Computer Interface ◽  
Assessment Scores ◽  
Before And After ◽  
Mean Differences

Background Brain–computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery. Methods A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model. Results We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies ( I2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm. Conclusion This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.

scholarly journals Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

2011 ◽  
Vol 6 (3) ◽  
pp. 403 ◽  
Author(s):  
Ioan Dzitac ◽  
Tiberiu Vesselényi ◽  
Radu Cătălin Ţarcă
Keyword(s):  
Fuzzy Inference ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Inference System ◽  
Eeg Data ◽  
Recent Developments ◽  
Potential Activity ◽  
Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

scholarly journals Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 11876
Author(s):  
Catalin Dumitrescu ◽  
Ilona-Madalina Costea ◽  
Augustin Semenescu
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Rapid Change ◽  
Training Session ◽  
Extraction Methods ◽  
Computer Interface ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Sensorimotor Rhythms

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

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 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.

scholarly journals Brain-Computer Interfaces for Internet of Things

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1179 ◽  
Author(s):  
Francisco Laport ◽  
Francisco J. Vazquez-Araujo ◽  
Paula M. Castro ◽  
Adriana Dapena
Keyword(s):  
Internet Of Things ◽  
Communication Protocol ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Event Classification ◽  
Computer Interfaces ◽  
Controlling Elements ◽  
Artefact Reduction ◽  
The Brain

A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.

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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