Neuroscience Research Equipment Brazil - Equipamento para Pesquisa em Neurociência e Comportamento

 
 

EEG | fMRI BOLD | QEEG | ERP | TMS | MEG | SLEEP | BCI | Eye Tracking | PAINNIRS SNA  |  VFC  | SNIFF | Free Software |

 
 
 

Fone: +55 11 3020-8066

E-mail: contato@brainsupport.com.br 

 
     Quem Somos
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    Brain Products
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    EEG fMRI
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    EEG ERP
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    Brain Computer
  Interface BCI

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    Behavioral
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    EEG TMS
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  Touca de Eletrodos
  Ativos actiCAP

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   BESA
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   BESA Research 5.3
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   BESA Epilepsy 1.0
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   EEGFocus 3.0
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    Brainvoyager
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 BrainVoyager QX

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 Turbo-BrainVoyager
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 TMS neuronavegador
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 Neurolator 3D
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 Eye Tracking
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  NeuroFeedback e
  Relaxamento

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 Power Mag Research

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 EEG MEG

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  EEG

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  Extreme EEG

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  TMS 30 Hz TMS 100 Hz

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  Acessórios

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    Capa de Eletrodos
  Ativos actiCAP

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    Capa ou Touca de
  Eletrodos BrainCAP

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    Capa ou Touca fMRI
  BrainCAP MR

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    Capa de Eletrodos
  EasyCap

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  Channels Amplifier

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    First Amp: 8 canais
  EEG, EOG, ECG, EMG,
  GRS

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           V-Amp: 8 ou 16 canais
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    QuickAmp: 40, 72 ou
  136 canais actiShield
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    BrainAMP Plus: DC ou
  AC 32, 64,  96, 128
  canais na  fMRI e 256
  canais fora da fMRI

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    BrainAMP MR: 32, 64
  96, 128 canais fMRI

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    BrainAMP DC: 32, 64
  96, 128, 160, 192
  224 ou 256 canais

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    BrainAMP Stander: 32
  64, 96, 128, 160, 192
  224 ou 256 canais

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         ExG MR:8 ou 16 canais
  na Ressonância - fMRI
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    ExG: 8 ou 16 canais
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    Amplificadores de EEG
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    Projects & References
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    Software para
  Aquisição e Análise

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Brain Machine Interface BMI  –  Brain Computer Interface BCI  Biofeedback

A BCI allows gaining information about the cognitive state of a human being without the necessity to produce any activity outside the central nervous system.

The first approach of using brain activity directly for communication comes from Vidal in the early 70s. First steps into the field of application have been realized by Birbaumer and Wolpaw in the 80s and 90s by building EEG-based support systems for patients suffering from amyotrophic lateral sclerosis (ALS).

The application of methods from statistical machine learning (Blankertz, Curio & Mueller 2002) for BCI had a deep impact on the accuracy of detecting patterns within the EEG data. Subsequently, it was possible to transfer the learning effort from the human being to the machine. Hence, it minimized the effort that is needed to develop the skills to control a BCI system.

When the focus is laid on the applicability of BCI while interacting in typical human-machine system environments the term Brain-Computer Interaction often is used instead of Brain-Computer Interface. User states hardly inferable from exogenous factors are estimated by the BCI, interpreted and fed back into the technical part of the system. The information about the user states can be handled as explicit (active BCI) or implicit (passive BCI) commands (Zander 2008).

For further information please visit our BCI Special Site.

 

 Neuroscience

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 Resonance Technology

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 Clinical MRI

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 Audio Stimulation fMRI

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 Video Stimulation fMRI

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 3D w/ Eyetracker fMRI

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 Sound Serenity MRI

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 Cinema Vision MRI 

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 NIRx Medical Technologies

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 NIRS 4 or 16 sensors

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 NIRS 16 or 32 detectors

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 NIRS 128 sensors

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 NIRS Accessories

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  WR Medical

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 Função Autonômica

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 Variabilidade da
 Frequência Cardíaca

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 Q-sweat

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 Sistema QST: Case IV

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 WR TILT TABLE

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 SMT SNIFF MAGNITUDE

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 Potencial Evocado
 Auditivo PEA

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 Potencial Evocado
 Visual PEV

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 Potencial Evocado
 Cognitivo

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 Potencial Evocado
 Somatosensitivo PESS

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 Potencial Evocado
 Olfatório

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 Potencial Evocado
 Gustatório

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 Potencial Evocado
 Viceral

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 Potencial Evocado por
 Dor Estimulada

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 Potencial Evocado
 Motor MEP

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 Estimuladores

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 Neuronavegador

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   Functional MRI
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   FuncLab Package MRI
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   Evoked Response Pad
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   Joystick 5 button MRI
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   Trackball MRI
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    Slim Headset MRI
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   Client List mrivideo
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   fMRI Accessories
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   Software Grátis
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   Free software
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   Pesquisas

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