Wednesday, April 18, 2018

Highly Interactive BCI based on Flicker-Free SSVEP

Visual evoked potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. 

Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.

For more information about BCI/EEG press here.

Tuesday, April 17, 2018

"BCI: Two Concurrent Learning Problems" by Maureen Clerc

Brain-Computer Interfaces (BCI) are systems which provide real-time interaction through brain activity, bypassing traditional interfaces such as keyboard or mouse. A target application of BCI is to restore mobility or autonomy to severely disabled patients. In BCI, new modes of perception and interaction come into play, which users must learn, just as infants learn to explore their sensorimotor system. Feedback is central in this learning. From the point of view of the system, features must be extracted from the brain activity, and translated into commands. (...) It is for instance possible to monitor the brain's reaction to the BCI outcome. In this talk I will present some of the current machine learning methods which are used in BCI, and the adaptation of BCI to users' needs.

For more information about BCI/EEG press here.

Thursday, April 05, 2018

BCI and EEG Projects in European Commission

It is the European Commission's primary public repository and portal to disseminate information on all EU-funded research projects and their results in the broadest sense. The website and repository include all public information held by the Commission (project factsheets, publishable reports and deliverables), editorial content to support communication and exploitation (news, events, success stories, magazines, multilingual "results in brief" for the broader public) and comprehensive links to external sources such as open access publications and websites.

If you want to know all the European Research Projects about a specified area, as "BCI" or "EEG", you can use the Community Research and Development Information Service (CORDIS). 
For example, to get all the results containning BCI, written in english, from September, 1 - 2017, type in the search field "('BCI') AND language='en' AND contentUpdateDate>=2017-09-01" or just use the "advanced search" option.

For more information about BCI/EEG press here.

Wednesday, April 04, 2018

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

PhyCS is the annual meeting of the physiological interaction and computing community, and serves as the main international forum for engineers, computer scientists and health professionals, interested in outstanding research and development that bridges the gap between physiological data handling and human-computer interaction. 

PhyCS brings together people interested in creating novel interaction devices, adaptable interfaces, algorithms and tools, through the study, planning, and design of interfaces between people and computers that are supported by multimodal biosignals. 


  • Biomedical Devices for Computer Interaction
  • Brain-computer Interfaces
  • Wearable Sensors and Systems
  • Cybernetics and User Interface Technologies
  • (...)

  • Biosignal Acquisition, Analysis and Processing
  • Simulation of Physiological Processes
  • Pattern Recognition
  • Neural Networks
  • (...)

  • User Experience
  • Usability
  • Adaptive Interfaces
  • Human Factors in Physiological Computing
  • (...)
  • Physiology-driven Computer Interaction
  • Biofeedback Technologies
  • Assistive Technologies
  • Physiological Computing in Mobile Devices
  • (...)

For more information about BCI/EEG press here.