Brain Computer Interfaces, more commonly known as BCIs, are the building blocks for all robotic assistive aids. It is by using BCIs that the robotic assistive aids can know and… Read more
On 8th October 2016, the world’s first Cybathlon took place in Zurich, Switzerland. The Cybathlon is a competition for people with disabilities using robotic assistive aids to complete tasks… Read more
The executive board of ETH Zurich has officially announced that Cybathlon will take place 2020 in Zurich! Also, discover the Cybathlon set of stamps issued by Sierra Leone & find out which Cybathlon image was selected as part of the BBC’s most striking photos of 2016.
09.02.17 – In today’s press conference with the executive board of ETH Zurich an official announcement was made: The Cybathlon will take place once again in Zurich in 2020 – organised by ETH ZurichBehind the scenes, planning for the next Cybathlon has been taking place since the successful premiere in October last year. The success …
07.12.16 – We are looking for two Postdocs in soft wearable robotics.*****Postdoc in Soft Wearable Robotics: Kinetic / Haptic feedback The EPFL Laboratory of Intelligent Systems (Prof. Dario Floreano, http://lis.epfl.ch) and the EPFL Translational Neural Engineering Lab (Prof. Silvestro Micera, http:// tne.epfl.ch) invite applications for a postdoctoral fellowship in wearable technologies for human-robot interaction. The postdoc will work …
30.11.16 – Congratulations to our spin fund winners Intento, who have won 130k CHF at Venture Kick. Read more.
15 Jun – 16 Jun 2017
Building Bodies for Brains & Brains for Bodies & 3rd Japan-EU Workshop on Neurorobotics
|Building Bodies for Brains & Brains for Bodies & 3rd Japan-EU Workshop on Neurorobotics Registration for both events now open.|
9 Oct – 12 Oct 2016
WORKSHOP ON BRAIN-MACHINE INTERFACES (SMC 2016)
Intercontinental Hotel, BUDAPEST, 1052 Budapest
|Please see: https://documents.epfl.ch/users/c/ch/chavarri/www/IEEESMC2016_BMI/BMI-IEEESMC2016.html|
Looking for publications? You might want to consider searching on the EPFL Infoscience site which provides advanced publication search capabilities.
This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user’s intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user’s mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol.
Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.
In this paper we argue that for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the user’s evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also consider the internal context, such as cognitive states and brain signal reliability. We demonstrate two successful approaches to modulating the level of assistance: by using online task performance metrics; and by monitoring the reliability of the BCI decoders. We then describe how these approaches could be fused together, resulting in a more user-centred solution.