Workshops Schedule - 7th International BCI Meeting, 2018

The 7th International BCI Meeting: “BCIs: Not Getting Lost in Translation”, is scheduled for May 21 – 25, 2018 at the Asilomar, California, USA.

SESSION 1- WEDNESDAY, MAY 23 (9:00-12:00)

• BCIs for stroke rehabilitation­­­
• Progress in Decoding Speech processes using intracranial signals
• Noninvasive BCI-control of FES for grasp restoration in high spinal cord injured humans
• Collaborative and Competing Multi-Brain BCI’s
• ECoG based BCIs
• Examining Ethical Assumptions About Neural Engineering and BCI Development
• Towards the Elusive Killer App for BCIs
• User-Centered Design in BCI development; A Broad Perspective
• ­­­­­­­­­­­­­­­Lower-limb brain-machine interfaces and their applications 

SESSION 2- THURSDAY, MAY 24 (9:00-12:00)

• BCIs for assessment of locked-in and DOC patients
• Turning negative into positives! Exploiting “negative” results in BCI research
• ­­­­­­­­Eye Tracking, Vision, and BCI
• Natural Language Processing & BCI
• ­­­­­­­­BCI and Augmented/Virtual Reality
• ­Recent Developments in Non-Invasive EEG SensorTechnology
• Making use of the future of BCI implant technology
• Clinical Applications of Brain-Computer Interfaces in Neurorehabilitation

SESSION 3- THURSDAY, MAY 24 (13:15-16:15)

• ECoG for control and mapping
• Real-time BCI communication for non-verbal individuals with cerebral palsy
• Tools for establishing neuroadaptive technology through passive BCIs
• Neurofeedback during Artistic Expression as Therapy
• Unsupervised Learning for BCIs
• Perception of Sensation Restored through Neural Interfaces
• From the lab into the wild: shaping methods and technologies for large-scale BCI research
• Standards for Neurotechnologies and Brain-Machine Interfacing

For more information about BCI/EEG press here.

28 Workshops from the 6th International BCI Meeting

The 6th International BCI Meeting was held 30 May–3 June 2016 at the Asilomar, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. To download the complete OpenAccess document in PDF format press here. 

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New features in "BCI over EEG" Blog

In this Blog header there is a new feature making possible to group the information by topics (BCI, EEG, Brain, Jobs, Conference,...) allowing a more organized search of the several published posts.

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Professor in Brain-Computer Interfaces and Neural Engineering

The School of Computer Science and Electronic Engineering from the University of Essex - UK, is looking for Professor in BCI and neural engineering. 

As part of the continued expansion of the School, we are seeking to appoint two Professorships in two of the four areas of specialism in the school. We are also recruiting in the areas of:

• Artificial Intelligence and Computer Games
• Cyber Physical Systems
• Human Language Technology/Natural Language Processing.

The successful candidate will have a PhD in a relevant discipline and relevant academic expertise in a related area. You will have a proven track record of internationally excellent research relevant to the post, a strong track record of published academic output at international levels of recognition, success in raising grant income appropriate to the research discipline, and a sustained record of effectiveness in relation to teaching and learning at both undergraduate and postgraduate levels.

For more information about BCI/EEG press here.

A Multi-APP Framework for BCI/EEG on Android Smartphones

If you want to process EEG on an Android smartphone then this paper EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone from School of Medicine and Health Sciences - University of Oldenburg, published in Biomed Research International, may be of great interest: .

Our aim was the development and validation of a modular signal processing and classification application enabling online EEG signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and Classification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach. In order to implement a closed-loop BCI on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.

For more information about BCI/EEG press here.

cEEGrids: Concealed, Unobtrusive Ear-Centered EEG Acquisition

Martin G. Bleichner and Stefan Debener from University of Oldenburg, Germany, created the cEEGrids: an EEG system motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. This new solution represents one more important step in the portability of EEG that can be used in a much more natural way.

We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. 

For more information about BCI/EEG press here.

BioSignals 2019 - 12th International Joint Conference on Biomedical Engineering Systems and Technologies

The purpose of the International Conference on Bio-inspired Systems and Signal Processing is to bring together researchers and practitioners from multiple areas of knowledge, including biology, medicine, engineering and other physical sciences, interested in studying and using models and techniques inspired from or applied to biological systems. (...) The analysis and use of these signals is a multidisciplinary area including signal processing, pattern recognition and computational intelligence techniques, amongst others.

UPCOMING DEADLINES

• Regular Paper Submission: October 1, 2018
• Regular Paper Authors Notification: November 29, 2018
• Regular Paper Camera Ready and Registration : December 13, 2018

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NoiseTag BCI - High Accuracy without Trainning

The Donders Research Group from Radboud University created a BCI/EEG platform which aims high accuracy, speed reaction and user-friendly. The main features improved are CCA-based Reconvolution, Dynamic stopping, Zero-training, Asynchronous, Headsets, Adaptive and Applications.

We found a method that turns BCI into plug and play. The first button you look at will take the system a bit longer to figure out, taking about 30 seconds. Then the second button goes down to 10 seconds. Then the 3rd-4th is down to 1-2 seconds. A person can get up to 1 button per second. 

The headband uses dry electrodes, so we do not have to use water.

In the Noise-Tagging project we utilize pseudo-random noise-codes as stimulation sequences (i.e., stimuli are tagged with noise) for evoked Brain BCI. These so-called noise-tags exhibit a spread-spectrum signal and when applied as stimuli, these evoke Broad-Band Evoked Potentials (BBEP) visible in the EEG. We have designed a generative method – Reconvolution – which combines both deconvolution and convolution to learn and predict responses to these noise-tags. Specifically, adhering to the superposition hypothesis, the complex BBEP can be decomposed into a summation of time-shifted versions of a/some transient response(s). 

For more information about BCI/EEG press here. 

10 EEG Headsets Technical Overview Comparison

The IMOTIONS company published a recent comparison about the 10 headsets more used in EEG research. Number of channels, Sampling Rate, Type of Communication Medically Certified are some of the characteristics analyzed.

Finding the right EEG device for your research can be a tricky process. There are a multitude of aspects to consider, and the importance of each can depend on your approach. To add to the confusion, each manufacturer shows (or doesn’t show!) the information in different ways, making the search for the right device even more difficult. Below, we’ve put together a listing of some of the most important variables that you’ll want to consider when investing in an EEG headset.

Headset Devices:

• Neuroelectrics Enobio 32 
• Emotiv Epoc 
• OpenBCI Cyton 
• ABM B-Alert X24 
• Brain Products ActiCHamp 
• AntNeuro eego 
• BioSemi 
• g.tec nautilus 
• Cognionics Mobile 72 
• mBrainTrain Smarting 

All this study can be found in IMOTIONS headset comparison.

For more information about BCI/EEG press here.