The Development Prospect of Brain Computer Interface
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Team 13's document summarizes research on non-invasive brain-computer interface (BCI) technology and applications. It discusses how BCI can be used to study emotional data and enable communication. Different non-invasive BCI methods like EEG, MEG, and fMRI are outlined. Current BCI applications include entertainment, smart home control, healthcare, and communication/control. The market for BCI is growing at 11% annually and companies like Emotiv are developing BCI products for capturing emotional metrics and brain typing on social media. Future work aims to connect emotional data detected through BCI with communication applications.
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Brain Computer Interface
The document discusses brain computer interfaces (BCI), which allow humans to control devices with their thoughts by detecting brain signals. It covers the history and principles of BCI, describing invasive and non-invasive techniques for acquiring brain signals like EEG, ECoG, and fMRI. Applications discussed include using BCI for gaming, robotics, medicine, and military purposes. Challenges include training users and improving signal acquisition, but proposed solutions involve selective attention strategies and signal preprocessing techniques. In conclusion, BCI technology continues to improve detection methods and provide new options for human-machine interaction.
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Brain-Computer Interface is a direct communication pathway between the brain and an external device, and as such contains a plethora of opportunity in scientific fields. This study assessed the feasibility of using a commercially available Emotiv EPOC+ headset interfaced with a radio console to allow for control of volume and tuning via mental commands. The development of a Brain-Computer Interface using the Emotiv EPOC+ headset was successful via pairing of mental commands to radio console actions, representing an initial step toward devices to allow disabled persons to interact with their environment. However, precision is currently limited by the ability of the BCI to interpret brain signals and properly configure hardware and software.
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brain chip technology is a technology which involves communication based on neural activity generated by the brain. brain chip technology implements the brain computer interface.
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This document summarizes research on using electroencephalography (EEG) brainwave signals to control home automation devices. It discusses several previous studies that used EEG signals to control robots, anticipate finger movements, control environmental devices for paralyzed patients, and classify gestures for home automation. The document then outlines a study that analyzed brainwave signals to detect patterns related to thoughts and emotions. These signals were sensed by a brainwave sensor, transmitted via Bluetooth, processed using Matlab, and used to send commands to control home devices. The goal was to develop a brain-computer interface for controlling home automation systems using EEG brainwave signals.
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Scanned by CamScannerScanned by CamScanner21.docx
Scanned by CamScanner
Scanned by CamScanner
2162-2248/15©2015IEEE34 IEEE ConsumEr ElECtronICs magazInE ^ july 2015
M
ajor thrusts combining neuroscience, sen-
sor chip design, and software development have
already shown remarkable advancement,
regardless of many uncertainties and challeng-
es. Brain–computer interface (BCI) headsets,
which are injecting new break points in games and entertain-
ment, deliver desirable special effects, aiding wellness train-
ing and rehabilitation. This article summarizes the brain
signal processing algorithmic approach in achieving the level
of interpretability of brain signals to date. The emergence of
imprecise brainwave headsets in the commercial world is
illustrated. Current tools for research and future development
are discussed, with a recommendation to standardize the
brain signal databank, anticipating its reach to big data.
Sheryl Flynn and I delivered a tutorial “Therapeutic Neuro-
game Application Development for Healthcare/Wellness” at
the 2015 International Conference on Consumer Electronics
(ICCE). Dr. Flynn, founder and chief executive officer of Blue
Marble Game Company, is a world-renowned expert with a
unique perspective on therapeutic neurogaming products.
Based on her experience and the feedback from the audience, it
behooves me to elaborate on the state of the art of BCI technol-
ogy and products.
BACKGROUND AND ROAD MAP
In 1929, neuroscientists started to observe the primary cur-
rents generated by synchronous firing of large populations of
neurons in the brain. The secondary currents induced as the
extracellular return currents are measurable as extracranial
electric potentials by electroencephalography (EEG), reflect-
ing human cognizance. These research activities intensified
after the 1970s. The Wadsworth Center in New York [15] cre-
ated a BCI system that incorporated electronic signals from
the brain into a novel communication-and-control device in
1991, using as many as 75 sensors. However, this device
required institutional electronic expertise, and it was very
costly. It took the next 20 or so years for a consumer-grade
product to be introduced. Since 2012, BCI headsets using one
Brain–Computer
Interface Technology
and Development
By Narisa N.Y. Chu
The emergence of imprecise
brainwave headsets
in the commercial world.
Digital Object Identifier 10.1109/MCE.2015.2421551
Date of publication: 15 July 2015
july 2015 ^ IEEE ConsumEr ElECtronICs magazInE 35
Im
ag
e lIc
en
sed
by In
g
r
am
pu
blIsh
In
g
to nine sensors have entered the market, with some examples
demonstrated in Figures 1 and 2, where sensor placements
have also been indicated for various settings based on standard
P300 EEG detection locations on the scalp. A road map that
highlights the BCI technology development is shown in Figure 3,
contrasting with the original research focus on brain signal
processing algorithms with the advent of many produc.
J04302076081
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Impact of adaptive filtering-based component analysis method on steady-state ...
The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
Brain computer interaction
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Brain computer interface based smart keyboard using neurosky mindwave headset
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Ijetcas14 393
This document summarizes an approach to embedding a human brain with smart devices using depreciated brain-computer interface (BCI) technology. It discusses how BCI systems work by acquiring EEG signals from the brain, preprocessing the signals, classifying them, and using them to control external applications. Specifically, it proposes controlling a tablet through a 1-channel EEG amplifier and non-invasive electrode placement. The document outlines the basic components and applications of BCI systems and describes implementing a basic prototype to test controlling a media player on a tablet using EEG signals processed in MATLAB.
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This document provides the program schedule for the 2015 IEEE International Conference on Consumer Electronics held in Taiwan from June 6-8, 2015. The conference included sessions on various topics related to consumer electronics on those dates in rooms IB-101, IB-201, IB-202 and other locations. Keynote speeches were given each day from 10:50-12:00 and other times. Sessions covered areas such as multimedia signal processing, security, big data analytics, wearables, medical applications and more. Social events included a welcome reception on June 6th and a banquet on June 7th.
BRAIN COMPUTER INTERFACE
1. A brain-computer interface (BCI) allows direct communication between the brain and external devices, helping people with motor impairments and providing new functionality.
2. BCI can be invasive, using implants in the brain to detect high-quality signals, but these are prone to scar tissue buildup. Non-invasive BCIs use neuroimaging techniques but produce poorer signals.
3. Experiments have used EEG to detect brainwaves and allow people to type or control devices through thought. As detection techniques improve, BCI could provide more alternatives for people to interact with their environment.
Final thesis presentation on bci
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Bci controlled calls
1. This document discusses brain-computer interfaces (BCI) which allow direct communication between the brain and external devices.
2. BCI research began in the 1970s with the goal of helping disabled patients control external devices with their thoughts.
3. The document then describes a specific BCI application using an EEG headset to control answering and rejecting incoming phone calls based on measured attention levels in the brain.
Brain Computer Interface with types and working
This presentation is about Brain Computer Interface with major historical events, working of Brain Computer Interface, types of Brain computer interface, Invasive Partial invasive and non invasive BCI.
Tells how BCI and HCI together can help to improve our life style,
applications of BCI and HCI,
Advantages and Benefits of Embrace with Considerations to keep in mind and future of BCI and HCI
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The document provides an overview of electroencephalography (EEG) and brain-computer interface (BCI) systems. It discusses EEG fundamentals including brainwaves, EEG definition, and commercial EEG devices. It also reviews the components of a BCI system including signal acquisition, preprocessing, feature extraction, and classification. The goal is to help understand EEG-based BCI systems for research purposes such as controlling robots.
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Scanned by CamScannerScanned by CamScanner21.docx
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Scanned by CamScanner
2162-2248/15©2015IEEE34 IEEE ConsumEr ElECtronICs magazInE ^ july 2015
M
ajor thrusts combining neuroscience, sen-
sor chip design, and software development have
already shown remarkable advancement,
regardless of many uncertainties and challeng-
es. Brain–computer interface (BCI) headsets,
which are injecting new break points in games and entertain-
ment, deliver desirable special effects, aiding wellness train-
ing and rehabilitation. This article summarizes the brain
signal processing algorithmic approach in achieving the level
of interpretability of brain signals to date. The emergence of
imprecise brainwave headsets in the commercial world is
illustrated. Current tools for research and future development
are discussed, with a recommendation to standardize the
brain signal databank, anticipating its reach to big data.
Sheryl Flynn and I delivered a tutorial “Therapeutic Neuro-
game Application Development for Healthcare/Wellness” at
the 2015 International Conference on Consumer Electronics
(ICCE). Dr. Flynn, founder and chief executive officer of Blue
Marble Game Company, is a world-renowned expert with a
unique perspective on therapeutic neurogaming products.
Based on her experience and the feedback from the audience, it
behooves me to elaborate on the state of the art of BCI technol-
ogy and products.
BACKGROUND AND ROAD MAP
In 1929, neuroscientists started to observe the primary cur-
rents generated by synchronous firing of large populations of
neurons in the brain. The secondary currents induced as the
extracellular return currents are measurable as extracranial
electric potentials by electroencephalography (EEG), reflect-
ing human cognizance. These research activities intensified
after the 1970s. The Wadsworth Center in New York [15] cre-
ated a BCI system that incorporated electronic signals from
the brain into a novel communication-and-control device in
1991, using as many as 75 sensors. However, this device
required institutional electronic expertise, and it was very
costly. It took the next 20 or so years for a consumer-grade
product to be introduced. Since 2012, BCI headsets using one
Brain–Computer
Interface Technology
and Development
By Narisa N.Y. Chu
The emergence of imprecise
brainwave headsets
in the commercial world.
Digital Object Identifier 10.1109/MCE.2015.2421551
Date of publication: 15 July 2015
july 2015 ^ IEEE ConsumEr ElECtronICs magazInE 35
Im
ag
e lIc
en
sed
by In
g
r
am
pu
blIsh
In
g
to nine sensors have entered the market, with some examples
demonstrated in Figures 1 and 2, where sensor placements
have also been indicated for various settings based on standard
P300 EEG detection locations on the scalp. A road map that
highlights the BCI technology development is shown in Figure 3,
contrasting with the original research focus on brain signal
processing algorithms with the advent of many produc.
J04302076081
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Impact of adaptive filtering-based component analysis method on steady-state ...
The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.
Brain computer interaction
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This document discusses brain-computer interfaces (BCI). It defines BCI and describes the different types - invasive, non-invasive, and semi-invasive. It explains the implementation process for BCI, including signal acquisition using EEG, feature extraction, translation to device commands, and feedback. Examples of BCI applications in India are provided. The global BCI market and conclusions are also briefly mentioned.
Ijetcas14 393
This document summarizes an approach to embedding a human brain with smart devices using depreciated brain-computer interface (BCI) technology. It discusses how BCI systems work by acquiring EEG signals from the brain, preprocessing the signals, classifying them, and using them to control external applications. Specifically, it proposes controlling a tablet through a 1-channel EEG amplifier and non-invasive electrode placement. The document outlines the basic components and applications of BCI systems and describes implementing a basic prototype to test controlling a media player on a tablet using EEG signals processed in MATLAB.
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1. A brain-computer interface (BCI) allows direct communication between the brain and external devices, helping people with motor impairments and providing new functionality.
2. BCI can be invasive, using implants in the brain to detect high-quality signals, but these are prone to scar tissue buildup. Non-invasive BCIs use neuroimaging techniques but produce poorer signals.
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applications of BCI and HCI,
Advantages and Benefits of Embrace with Considerations to keep in mind and future of BCI and HCI
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Program for 2015 ieee international conference on consumer electronics taiw...
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HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
Class 10 human eye notes physics
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Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
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seed production, Nursery & Gardening.pdf
This presentation offers a general idea of the structure of seed, seed production, management of seeds and its allied technologies. It also offers the concept of gene erosion and the practices used to control it. Nursery and gardening have been widely explored along with their importance in the related domain.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
Gadgets for management of stored product pests_Dr.UPR.pdf
Insectsplayamajorroleinthedeteriorationoffoodgrainscausingbothquantitativeandqualitativelosses
Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia
Simpletechnologiesfortimelydetectionofinsectsinthestoredproduceandtherebyplantimelycontrolmeasures
The cost of acquiring information by natural selection
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Signatures of wave erosion in Titan’s coasts
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
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Embracing Deep Variability For Reproducibility and Replicability
Embracing Deep Variability For Reproducibility and ReplicabilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
Embracing Deep Variability For Reproducibility and Replicability
Abstract: Reproducibility (aka determinism in some cases) constitutes a fundamental aspect in various fields of computer science, such as floating-point computations in numerical analysis and simulation, concurrency models in parallelism, reproducible builds for third parties integration and packaging, and containerization for execution environments. These concepts, while pervasive across diverse concerns, often exhibit intricate inter-dependencies, making it challenging to achieve a comprehensive understanding. In this short and vision paper we delve into the application of software engineering techniques, specifically variability management, to systematically identify and explicit points of variability that may give rise to reproducibility issues (eg language, libraries, compiler, virtual machine, OS, environment variables, etc). The primary objectives are: i) gaining insights into the variability layers and their possible interactions, ii) capturing and documenting configurations for the sake of reproducibility, and iii) exploring diverse configurations to replicate, and hence validate and ensure the robustness of results. By adopting these methodologies, we aim to address the complexities associated with reproducibility and replicability in modern software systems and environments, facilitating a more comprehensive and nuanced perspective on these critical aspects.
https://hal.science/hal-04582287
Recently uploaded (20)
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
Anti-Universe And Emergent Gravity and the Dark Universe
Anti-Universe And Emergent Gravity and the Dark Universe
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...
Gadgets for management of stored product pests_Dr.UPR.pdf
Gadgets for management of stored product pests_Dr.UPR.pdf
The cost of acquiring information by natural selection
The cost of acquiring information by natural selection
Nutaceuticsls herbal drug technology CVS, cancer.pptx
Nutaceuticsls herbal drug technology CVS, cancer.pptx
Clinical periodontology and implant dentistry 2003.pdf
Clinical periodontology and implant dentistry 2003.pdf
Microbiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdf
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...
Embracing Deep Variability For Reproducibility and Replicability
Embracing Deep Variability For Reproducibility and Replicability
The Development Prospect of Brain Computer Interface
- 1. Team 13 Sanghyun Lee, Cherin Lim, Jungsoo Yun, Chenxin Luo
- 2. Contents Reseach Question Explanation of Non-invasive BCI BCI Application Our next step
- 3. ResearchQuestions Q. How is emotional data being used in BCI? Q. How is communication enabled through BCI?
- 4. TypesofNon-invasiveBCITechnology= least resistant BCI Using the differences in blood oxygen concentration due to nerve cell activation EEG Imaging of electromagnetic field by cerebral cortex neuron activity MEG fMRI Imaging of electromagnetic radiation by cerebral cortex neuron activity
- 5. • Removing noise signals using pre-treatment filters • Converting to frequency spectrum by using Fourier transform and band filter BCIProcess
- 6. EEG MEG fMRI Time Resolution Space Resolution Portability & Convenience Development Potential CharacteristicsofNon-invasiveBCI Then, How it is used now?
- 7. BCIApplication–MarketAnalysis Year 2012 2013 2014 2015 2016 2017 Entertainment 62.7 70.7 79.7 89.9 101.3 114.2 Communication/Control 148.6 164.9 183 203.1 225.4 250.1 Smart Home 9.4 10.7 12.2 13.9 15.9 18.1 Health Care 361.4 402.7 448.7 499.9 557 620.7 Total 582.1 649.0 723.6 806.8 899.6 1003.1 Early adopters investigate Growth Rate : 11% Early adopters : Emotiv, Facebook etc. Anticipation of approx.15 years Gartner Hype Cycle for Emerging Technologies: 2013~2017 (Reported annually in July) 0 100 200 300 400 500 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 MarketSizeof Communications Year <BCI Market Size, Application Analysis, Grand View Research>
- 8. BCIforEmotionalData Neurowear Tokyo based project team "communication for the near future" • Neuro-tagging map (for smartphones) • Brain Bookmark “you think, therefore, you can” • Epoc: Headset • MyEmotiv (Captures 6 key emotional metrics) Emotiv Manufactures EEG products
- 9. BCIforCommunications BCI in Communications Project Verbal Communication • direct communication between two people by reading brain activity • translation of envisioned actions to binary code ex) moving hands= 1, moving feet=0 Social Media (Brain Typing) • Facebook “Building 8” • BCI 2000-Twitter Interface (India to France, Harvard Medical School, 2013)
- 11. Reference 뇌파신호 및 응용 기술 동향 [김도영 외 5인, 2017 한국전자통신연구원] 뇌-컴퓨터 접속 장치 기술의 현재와 미래 [민병경, 뇌과학, 경계를 넘다] https://www.gartner.com/smarterwithgartner/top-trends-in-the- gartner-hype-cycle-for-emerging-technologies-2017/ https://www.cnet.com/news/brain-to-brain-verbal- communication-in-humans-achieved-for-the-first-time/ https://www.grandviewresearch.com/industry-analysis/brain- computer-interfaces-market http://neurowear.com/projects_detail/neuro_tagging_map.html https://www.emotiv.com/epoc/