Independent Studies - EMOTIV

The scientific study "Brain-Computer Interfaces by Electrical Cortex Activity: Challenges in Creating a Cognitive System for Mobile Devices Using Steady-State Visually Evoked Potentials" is referenced on the EMOTIV company website.

The independent study presents the BCI results obtained by reading the concentration state and SSVEP using an EPOC+ equipment.

For more information about BCI/EEG press here.

BCI and Neurotechnology - gTEC solutions

As most readers of this blog knows, gTEC is an Austrian company dedicated for several years to the BCI research. At present it has a very wide catalog of EEG equipment as well as signal processing software. As a user of these company solutions and also in the support that I have always obtained from the technical team I consider gTEC an excellent investment option in BCI research area.

For more information about BCI/EEG press here.

Where is the Consciousness Brain Region?

Elisa Clement published a new article in the AR Magazine from Champalimaud Foundation, Lisbon - Portugal. 

Most scholars consider consciousness to have two components: wakefulness and awareness. Wakefulness is fairly easy to define and measure experimentally, through EEG, because the pattern of activity shown by EEG is different in brains of awake subjects, compared to subjects who are asleep.

www.gregadunn.com

Awareness, on the other hand, is neither easy to assess, nor to define – what exactly does it mean to be “aware” of your surroundings? Are there different stages or levels of awareness? Are there any other more accurate and objective ways of assessing awareness other than through questionnaires filled in by subjects, which is how it is often assessed?

But the most crucial and interesting question yet to be answered is the following: is consciousness generated through the orchestrated activation of multiple brain areas, or is there one particular area responsible for it?

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3 BCI Jobs - Inria Bordeaux Sud Ouest

3 New BCI vacancies are available to start in September / October 2018:

• Computational Modeling of Mental Imagery-based BCI user training - PhD position;
• Redefining EEG Signal Processing and Machine Learning to ensure efficient Mental Imagery-based BCI user training - Post-doc position;
• R&D Engineer position to implement brain signal processing tools and BCI applications in the OpenViBE software platform - Engineer position. 

For more information about BCI/EEG press here.

EPOC Flex - The new EEG device from Emotiv

EMOTIV company has a new EEG device available for $2100. The EPOC Flex as 32 channels (+ 2 references), CMS/DRL configurable in any 10-20 location or on ears, 1024 internal downsampled to 128 SPS, realtime CQ monitor (patented), 0.16 – 43Hz, 16 bit per channel, 0.51μV @ 16 bit,  ±4.17 mV, sintered silver-silver chloride, 9 axis sensor, proprietary 2.4GHz wireless and li-poly battery, 680 mAh up to 9 hours.

EPOC Flex combines the award-winning wireless technology of our EPOC+ headset with the flexibility and high density afforded by more traditional EEG head cap systems. EPOC Flex is a wireless control box that works alongside the EasyCap system. It can be configured to record from any of the standard 10-20 EEG positions for up to 32 channels.

For more information about BCI/EEG press here.

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.

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.

Artificial Intelligence + Machine Learning = Deep Learning EEG

How can we apply AI and Machine Learning to EEG data? There is evidence that EEG characteristics can be used as an indication (a biomarker) of some diseases. For example, in a project funded by The Michael J. Fox Foundation, our findings indicate that there are significant differences in the EEG data of different RBD patients compared to healthy populations. More specifically, RBD subjects as a group had larger power in the frontal EEG electrodes than healthy subjects. Again taken as a group. Therefore, there is statistical significance in the difference between one group and the other. 

However, if we want to use this as a means for diagnosis, we need to take into account that diagnostic decisions are made on individuals, not on groups. For this to happen we need a decision system. We would input the data of a particular individual subject. Then we would get an answer on whether this individual is likely to develop, for instance, a neurodegenerative disease. Here is where Machine Learning and Deep Learning come into play.

For more information about BCI/EEG press here.

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

For more information about BCI/EEG press here.

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.

JASP - Open-source project for Data Analysis resulting in Fast Conclusions with Minimal Work

JASP is an open-source project supported by the University of Amsterdam with an intuitive interface offering standard data analysis in classical or bayesian form.

JASP was designed with the user in mind: APA-formatted tables can be copy-pasted in your word processor, output can be extensively annotated, adjustment of input options dynamically changes the output, and selecting old output revives the associated input choices for inspection and adjustment.

JASP is open-source and free of charge, and we provide it as a service to the community. JASP is also statistically inclusive, as it offers both frequentist and Bayesian analysis methods. Indeed, the primary motivation for JASP is to make it easier for statistical practitioners to conduct Bayesian analyses.

Feature List:

For more information about BCI/EEG press here.

OpenVibe - One of the Best BCI Software

OpenViBE is a free software platform dedicated to design, process and classify EEG data to be used in brain-computer interfaces. The package includes some tools to create and run custom applications (drag & drop), it is compatible with many EEG devices and demo programs are ready for use. Programming in Python is also possible.

OpenVibe is, in my opinion, one of the best software platform in BCIs so, if you want to learn how to use it, the tutorial from MENSIA enterprise is the best way to start. 

OpenViBE Consortium is the future management and funding structure for OpenViBE. Interested parties can join the consortium as members or donate to it. The consortium will be a non-profit that uses its funds to hire dedicated engineers. The engineers in turn develop the platform to directions that are of interest to the consortium members.

For more information about BCI/EEG press here.

NeuroTechNix 2018 Congress - Neuro: Prosthetics, Imaging, Sensing, Informatics, Computing, Modulation and Engineering

Neurotechnology shows a very high potential of enhancing human activities, involving technologies such as neural rehabilitation, neural prosthesis, neuromodulation, neurosensing and diagnosis, and other combinations of neurological and biomedical knowledge with engineering technologies.

Congress Areas

AREA 1: NEURAL REHABILITATION AND NEUROPROSTHETICS

• Assistive Technologies
• Telerehabilitation
• Virtual Reality Tools
• Augmentative and Alternative Communication
• Biofeedback Therapy
• Brain/Neural Computer Interfaces
• Clinical and Social Impact of Neurotechnology
• Human Augmentation
• Mobile Technologies
• Privacy, Security and Neuroethics
• Robotic Assisted Therapy

AREA 2: NEUROIMAGING AND NEUROSENSING

• Artificial Intelligence for Neuroimaging
• Sleep Analysis
• Brain imaging
• Diagnostic Sensors
• EEG and EMG Signal Processing and Applications
• Intelligent Diagnosis Systems
• Mobile and Embedded Devices
• Neural Signal Processing
• Positron Emission Tomography
• Real Time Monitoring of Neuromuscular and Neural Activity

AREA 3: NEUROINFORMATICS AND NEUROCOMPUTING

• (Artificial) Neural Networks
• Brain Models and Functions
• Cognitive Science and Psychology
• Computational Neuroscience
• Learning Systems and Memory
• Neurobiology
• Reverse Engineering the Brain
• Self-organization and Evolution

AREA 4: NEUROMODULATION AND NEURAL ENGINEERING

• Biochips and Nanotechnology
• Transcranial Magnetic Stimulation
• Translating into Clinical and Industrial Outcomes
• Bionic Vision
• Cochlear implants
• Cybernetics
• Deep Brain Stimulation
• Functional Electrical Stimulation (FES)
• Neuro-interface Prosthetic Devices
• Non-Invasive Brain Stimulation
• Optogenetics

The congress will be held in Seville - Spain, from 20 to 21 September 2018 and the Paper submission is May 2, 2018

For more information about BCI/EEG press here.

Open-Source Python Code for BCI/EEG

Visbrain is an open-source python 3 package dedicated to brain signals visualization. It is based on top of VisPy and PyQt and is distributed under the 3-Clause BSD license.

Visbrain includes six visualization modules :

• Brain : visualize EEG/MEG/Intracranial data, connectivity in a standard MNI 3D brain 
• Sleep : visualize polysomnographic data and hypnogram edition 
• Signal : data-mining module for time-series inspection 
• Topo : display topographical maps 

For more information about BCI/EEG press here.

Massachusetts Medical School aims to introduce Mindfulness into Medical Care

Dr. Judson Brewer, has been named as chief of the Division of Mindfulness. “The creation of a stand-alone Division of Mindfulness embedded within a Department of Medicine highlights how far the field has progressed and matured, and will create the infrastructure and support for researchers dedicated to furthering our neuroscientific knowledge of how the mind works, and for what medical conditions mindfulness is efficacious,” 

The move comes more than 30 years after the center’s Mindfulness Based Stress Reduction (MBSR) clinic was established at the university. Since then, more than 24,000 people have been trained in Mindfulness-based Stress Reduction (MBSR) at the clinic.

There are several studies on mindfulness meditation someones using electroencephalography. One of the major challenges is to prove scientifically functional changes in the cerebral cortex by comparing results on individuals during, at least, 8 weeks of therapy.

For more information about BCI/EEG press here.

Intheon are Wiring for BCI/EEG Data Processing

At Intheon, our vision is to embed advanced neurotechnology into everyday life. We offer a middleware platform for biosignal interpretation, which is easily integrated into existing mobile and desktop applications through a cloud API. NeuroScale™ empowers developers to rapidly create transformative brain- and body-aware applications impacting medicine and health, interactive technology, marketing, education, and more.

Deep Learning Research Engineer – Neurotechnology

We are seeking a highly talented and motivated research engineer who is fascinated by the latest developments in ML/AI and is looking for the most exciting, challenging, and high-impact areas to apply them. You will be part of team of senior researchers and engineers where you will focus on designing and implementing advanced brain-computer interface technology using state of the art deep learning techniques. Your research & development will help power the coming generation of brain-based human-machine interfaces where neurotechnology is integrated into everyday life.

• You will work directly with senior staff on developing new methods for applying deep learning on EEG and other physiological data.
• You will write production-grade code and train Brain-Computer Interface models on large amounts of EEG data.

Staff Scientist – Neuroscientist

Taking advantage of large-scale electrophysiological (e.g. EEG) data is essential for training robust powerful machine learning systems capable of decoding brain state in complex environments. At Intheon you will be responsible for developing and applying technologies for large-scale EEG data analysis, powering the coming generation of brain-based human-machine interfaces where neurotechnology is integrated into everyday life.

• You will work directly with our senior staff on advancing the state of the art in large-scale EEG data processing and management.
• You will develop new computational methods for combining EEG features across multiple studies (meta-analysis).
• You will interpret analysis results and publish them in scientific journals.

For more information about BCI/EEG press here.