An intelligent way to play music by brain activity using brain computer interface

2016 ◽  
Author(s):  
Bhawna Arora ◽  
Tanupriya Choudhury ◽  
Praveen Kumar ◽  
Mukherjee
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface

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.

scholarly journals Could Brain-Computer Interface be a new therapeutic approach for Body Integrity Dysphoria?

2021 ◽  
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, characterised 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, aetiology, 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 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 THE IMPACT OF ADVERTISEMENT ON CONSUMER’S PERCEPTION

2017 ◽  
Vol 5 ◽  
pp. 187-191
Author(s):  
Martin Hudák ◽  
Radovan MadleĹˆĂˇk ◽  
Veronika Brezániová
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Consumer Perception ◽  
Computer Interface ◽  
Market Situation ◽  
And Behavior ◽  
The Impact ◽  
The Brain ◽  
Tracking Technology ◽  
Wireless Eeg

Marketing can be described as a tool for companies to influence the consumer’s perception to the desired direction. The current market situation is characterized by dynamism, growing consumer power, and intense competition. The consumer perception and behavior are changing and therefore need to be constantly monitored and measured. The aim of this article is to scan and measure consumer’s perception while watching a video advertisement. During this experiment, an eye-tracking technology was used, which allows capturing a consumer’s gaze. The central part of the research is to measure the brain activity of a consumer based on the EEG (Electroencephalography). EMOTIV Epoc+ is a 14-channel wireless EEG, designed for contextualized research and advanced brain computer interface applications. An advertising campaign from four different mobile operators was used for this purpose. In the conclusion of this article, consumer’s perception of different advertising campaigns are compared and evaluated.

Harnessing Brain Signals for Communication

ASHA Leader ◽  
2013 ◽  
Vol 18 (1) ◽  
Author(s):  
Yael Arbel
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Signals ◽  
Interface System

The P300 Brain Computer Interface system converts people’s brain signals into words on a screen, enabling people who are completely paralyzed to communicate. The system records and analyzes brain activity in real time as the user focuses on conveying the intended message. The researchers behind this technology are working to develop a simplified version for efficient home use.

Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

2017 ◽  
Vol 43 (5) ◽  
pp. 501-511 ◽  
Author(s):  
A. A. Frolov ◽  
G. A. Aziatskaya ◽  
P. D. Bobrov ◽  
R. Kh. Luykmanov ◽  
I. R. Fedotova ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface
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