Wednesday, April 19, 2017

MMI - Mind-Machine Interfaces

 What are the possible socioeconomic impacts of a mind-machine interface?

Elon Musk described it as a “digital layer above the cortex that could work well and symbiotically with you,” in a way similar to the concept of cyberbrain in Ghost in the Shell (...). Neural lace would act as an interface which would regulate data exchange without permitting unfettered access - to prevent the mind from becoming “house cats” to AI.


Once this technology reaches maturity, our way of life would undergo a revolution:

  • Learners can “go to school” by allowing the interface to direct experiences and thoughts into consciousness - instead of reading, listening and interpreting from our sensory feeds.
  • A new profession would arise: mind-workers; with low-cost training (patterns fed into our brains), individuals can “lease” their “mind hour” to institutions via the MMI.
  • Once programs and systems develop to interface with large number of mind-workers, this would become a new way of employment with only one qualification: a functioning mind.
  • Human resources would demand a premium. Instead of technology replacing human labor, they now become enablers of broader employment.
  • Feelings and thoughts would become commodities for exchange and purchase, leading to new media and entertainment industries.

For more informations about BCI/EEG press here.


Tuesday, April 18, 2017

3D Brain Anatomy

The company Genes to Cognition Online offers a free 3D Brain Anatomy Model, as it shows in the following picture, compatible with almost Web browsers (no Java code!).


For more information about BCI/EEG press here.


Monday, April 17, 2017

OpenVibe - Job offers

OpenVibe are looking for new professionals to its staff:
  • C++ software engineer;
  • 2 Engineers for medical certification;
  • Postdoc in real-time 3D brain-activity visualization (EEG);
  • 3 Open engineer positions to work on BCI and the OpenViBE platform;
  • Post-doctoral fellow at Inria Bordeaux on BCI and physiological computing;
  • Postdoc/software engineer.

For more informations about BCI/EEG press here.


Wednesday, April 12, 2017

Low-cost and Portable BCI/EEG Equipment

Conventional BCIs are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.


This BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using 4 EEG channels) and a conventional 32-channel BCI.

For more informations about BCI/EEG press here.


Monday, April 10, 2017

New applications through revolutionary BNCI technologies

Digital Economy & Society department from European Commission shows an article about BNCI - Brain-Neural-Computer-Interaction. This area investigates how brain activity can be recorded and used to interact with an electronic device. It uses electroencephalography (EEG) in combination with electromyography (EMG) captured from muscles electrical signals.


BNCI is an information channel for sending messages and control commands direct from the brain to theexternal world. The critical progress needed for BNCI proliferation is not only in technical terms:
  • Economy of scale – BNCI should be made useful to a wider group of users. Beyond the design for people with disabilities, a possible spill over to mainstream technologies could reduce costs and increase performance.
  • Cool Design – In BNCI systems, in particular, the sensor should be cosmetically appealing to a broad range of users. Also, usability is of primary importance in people's perception.
  • Zero Training – Preparation time and the presence of experts to set up BNCI should be reduced in the future – ideally the user can use the BNCI system independently.
  • Interdisciplinary research - Stimulating increased networking and co-operation among engineering disciplines, computer science, neuroscience, psychology, medicine and bio-physiology.
For more informations about BCI/EEG press here.


Friday, April 07, 2017

BCI Research Groups

List of 50 BCI Research Groups all over the World

  1. Graz BCI Lab - Graz University of Technology, Austria
  2. The Brain Interface Project - University of British Columbia, Canada
  3. Institute of Biomaterials and Biomedical Engineering - University of Toronto, Canada
  4. Human Machine Interaction group - Shanghai Jiao Tong University, China
  5. BCI Group - Aalto University, Finland
  6. Hybrid - Inria Rennes, France
  7. Berlin BCI Group - Technical University of Berlin, Germany
  8. Brain-Machine Interface Research - University of Freiburg, Germany
  9. Brain State Decoding Lab - University of Freiburg, Germany
  10. Würzburg BCI Research Group - University of Würzburg, Germany
  11. Biomedical Engineering research group - University of Ireland Maynooth, Ireland
  12. Brain Machine Interface Lab - Instituto Italiano di Tecnologia, Italy
  13. Laboratory for Advanced Brain Signal Processing - Riken Brain Science Institute, Japan
  14. BCI Lab Group - University of Tsukuba, Japan
  15. Touyama's BCI laboratory - Toyama Prefecture University, Japan
  16. BCI Group - University of Engineering & Technology, Pakistan
  17. Laboratory for Neuroergonomics and BCI - NCR Kurchatov Institute, Russia
  18. I2R BCI Laboratory - Institute for Infocomm ResearchBCI, Singapore
  19. Sinapse - National University of Singapore, Singapore
  20. BCI Lab @ Unist - Ulsan National Institute of Science and Technology, South Korea
  21. Brain Signal Processing Laboratory - Korea University, South Korea
  22. BCI Group - Korea University, South Korea
  23. BDigital eHealth R&D - Barcelona Digital Centre Technològic, Spain
  24. BitBrain R&D - BitBrain Technologies, Spain
  25. Brain-Machine Interface Systems Lab - Miguel Hernandez University of Elche, Spain
  26. BCI Lab UGR - University of Granada, Spain
  27. UMABCI Lab - University of Málaga, Spain
  28. BCI research team - University of Zaragoza, Spain
  29. Non-Invasive Brain-Machine Interface Group - École Polytechnique de Lausanne, Switzerland
  30. BCIs Group - University of Essex, UK
  31. BCI Project - University of Oxford, UK
  32. Brain Cognition Computing Lab - University of Kent, UK
  33. Signal Processing and Control Group - University of Southampton, UK
  34. BCI and Assistive Technology - University of Ulster, UK
  35. BCI Laboratory - Colorado State University, USA
  36. ETSU BCI Laboratory - East Tennessee State University, USA
  37. Brain UI Group - Georgia Tech & Georgia State University, USA
  38. The BrainLab - Kennesaw State University, USA
  39. BCIs - Microsoft, USA
  40. The BCI project - Neil Squire Society, USA
  41. BCI Lab - Purdue University, USA
  42. Human-Computer Interaction Lab - Tufts University, USA
  43. De Sa BCI Lab - University of California, USA
  44. Swartz Center for Computational Neuroscience - University of California San Diego, USA
  45. Direct Brain Interface Laboratory - University of Michigan, USA
  46. Center for Neuro-Engineerig & Cognitive Science - University of Houston, USA
  47. Schalk Lab - Wadsworth Center, USA
  48. Wadsworth BCI Lab - Wadsworth Center, USA
  49. NeuroEngineering & BioMedical Instrumentation Lab - John Hopkins University, USA
  50. Aspen Lab - Old Dominion University, USA
For more informations about BCI/EEG press here.



Thursday, April 06, 2017

Matlab code to learn Recurrent Waveforms within EEGs

When experts analyze EEGs they look for landmarks in the traces corresponding to established waveform patterns, such as phasic events of particular frequency or morphology. This modeling approach automatically learns the waveforms corresponding to transient, reoccurring events within EEG traces. 


The methodology is based on a sparsely excited model of a single EEG trace, and the model parameters are estimated using shift-invariant dictionary learning algorithms developed in the signal processing community. On the motor imagery dataset, linear discriminant analysis can distinguish the type of motor imagery based on the spatial patterns of a subset of the learned waveforms.

For more informations about BCI/EEG press here.


Wednesday, April 05, 2017

EEG Source Imaging - Decoding Right Hand Motor Imagery Tasks

Brain-computer interfaces (BCIs) based on sensorimotor rhythms (SMRs) have achieved successful control, therefore there is a need to develop techniques which can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device. Over the past decade EEG source imaging (ESI) techniques have proven to be an effective approach for interpreting motor intent by reconstructing the current density on the cortical surface. 


The successful separation of these complex tasks in an offline setting provides confidence for developing an SMR BCI for the natural control of external devices using realistic motor imaginations. 

For more informations about BCI/EEG press here.


Tuesday, April 04, 2017

Teegi - a Tangible EEG Interface

Teegi is an anthropomorphic and tangible avatar exposing a users’ brain activity in real time. It is connected to a device sensing the brain by means of electroencephalography (EEG). Teegi moves its hands and feet and closes its eyes along with the person being monitored. It also displays on its scalp the associated EEG signals, thanks to a semi-spherical display made of LEDs. 


Attendees can interact directly with Teegi – e.g. move its limbs – to discover by themselves the underlying brain processes. Teegi can be used for scientific outreach to introduce neurotechnologies in general and brain-computer interfaces (BCI) in particular.

 For more informations about BCI/EEG press here.