Brain-Computer Interfaces (BCI) research are emerging in the last few years providing non-invasive, wireless and low-cost ElectroEncephaloGraphy (EEG) devices. Scientific papers are published almost every day providing new BCI solutions. Follow this evolution, shrouded in neuroscience, with a readout accessible to everyone.
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.
Theindependent studypresents the BCI results obtained by reading the concentration state and SSVEP using an EPOC+ equipment.
With the uninterrupted advancement of technology, BCIs also come up with new solutions that are increasingly available outside laboratories or hospital environments. The company Pantech Solutions has a recent article dedicated to this subject. As a few examples we have:
Brain Controlled Wheel Chair;
Drowsy Driver Detection;
Brainwaves for Neuromarketing;
Brain Controlled Automatic Braking System;
Press here to read the Top 25 BCI Projects 2018 published by the Pantech 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.
The Carnegie Mellon Forum on Biomedical Engineering, to be held at Carnegie Mellon University in Pittsburgh on September 21, 2018, will provide a platform for discussions and identification of grand challenges and frontiers in biomedical engineering research, education, and translation.
Microphysiological Models that Rely on Emergence in Multi-Cellular Engineered Living Systems;
World’s Deepest-Penetration and Fastest Cameras: Photoacoustic Tomography and Compressed Ultrafast Photography;
Challenges and Trends in Medical Robotics;
Driving and Reshaping Biotechnology;
Tackling Grand Challenges in Precision Medicine Through Biomedical Engineering;
Artificial Intelligence: Implications for Advanced Imaging and Precision Medicine;
Challenges in Translating BME Technologies;
Challenges in BME Education;
CMU Approach to Addressing BME Challenges;
Plenary Panel Discussion;
The forum will consist of keynote and plenary talks, plenary panel discussions, and poster presentations in the frontiers of biomedical engineering. A poster award competition will be open to students, postdocs or residents who present their research in any area interfacing engineering with medicine and health. Selected oral and poster presentations will be invited for submission to a special issue in IEEE Transactions on Biomedical Engineering. Faculty, graduate and undergraduate students, postdocs, residents, clinicians and industrial practitioners, within and outside of CMU, are all cordially invited.
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.
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?
An interesting opportunity for research scholarship starting at 2019 is available to post-graduate researchers that are planning to go to Switzerland to do research at doctoral or post-doctoral level. Only candidates nominated by an academic mentor will be considered and must be born after 1982.
Each year the Swiss Confederation awards Government Excellence Scholarships to promote international exchange and research cooperation between Switzerland and over 180 other countries. Recipients are selected by the awarding body, the Federal Commission for Scholarships for Foreign Students (FCS).
A very interesting 30 minutes presentation about BCI / Machine Learning and NeuroScience explained by Professor Aaron Batista.
When we learn, the brain changes at nearly every level of organization. Synapses form and strengthen, individual neurons change their tuning properties, and cortical maps expand. My research examines how learning alters the coordinated activity of populations of neurons. This is a particularly important level at which to study learning because it is the action of populations of neurons that drive behavior, generate perceptions, and undergird our cognition.
In the 7th International BCI meeting at Asilomar, California - USA, many papers presentations took place during the congress. This PDF provided by BCI Society, with more than 200 pages, describes a resume of each poster and exhibitor demonstrations divided by the following themes:
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.
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.
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.
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.
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.
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.
The Allen Institute for Brain Science has added the first data from human nerve cells to the Allen Cell Types Database: a publicly available tool for researchers to explore and understand the building blocks of the human brain. This first release includes electrical properties from approximately 300 living cortical neurons of different types derived from 36 patients, with accompanying 3D reconstructions of their shape or anatomy for 100 cells, and computer models simulating the electrical behavior of these neurons.
The database will also contain gene expression profiles, based on measurements of all genes used by 16,000 individual cells, from three adult human brains. Data from these human cells provide an unparalleled window into the intricate components, circuitry and function of the human neocortex, including features that make our brains unique.
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.
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.
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.
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.
The purpose of the International Conference on Bio-inspired Systems and Signal Processingis 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.
Regular Paper Submission: October 1, 2018
Regular Paper Authors Notification: November 29, 2018
Regular Paper Camera Ready and Registration : December 13, 2018
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).
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.
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.
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.
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.
AREA 1: NEURAL REHABILITATION AND NEUROPROSTHETICS
Virtual Reality Tools
Augmentative and Alternative Communication
Brain/Neural Computer Interfaces
Clinical and Social Impact of Neurotechnology
Privacy, Security and Neuroethics
Robotic Assisted Therapy
AREA 2: NEUROIMAGING AND NEUROSENSING
Artificial Intelligence for Neuroimaging
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
Learning Systems and Memory
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
Deep Brain Stimulation
Functional Electrical Stimulation (FES)
Neuro-interface Prosthetic Devices
Non-Invasive Brain Stimulation
The congress will be held in Seville - Spain, from 20 to 21 September 2018 and the Paper submission is May 2, 2018
The Imperial College of London has a Next Generation Neural Interfaces research group to "create interfaces that will change the way the people interact with surroundings". Another team of researchers that surely contribute in advancing of BCIs area.
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.
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.
The Brain-Computer Interfaces (BCI) are systems that allow through the thought to execute commands on computers or electronic devices. This technology, based on the reading of brain electrical impulses and its signal processing, is becoming more and more accessible to ordinary people when it was previously confined to enclosed spaces such as research laboratories or hospital clinics.
When we talk about BCIs, we have great contributions to its dissemination through Elon Musk and Mark Zuckerberg. These, in recent comments, have given as examples cars monitored with the mind or writing by thought. While the company Neuralink is recruiting experts in this field, the multinational Facebook has created a research department aimed at implementing a solution that allows you to write 100 words per minute on a computer, tablet or smartphone, just thinking. With the daily technological advance, associated with artificial intelligence increasingly present in data processing, the question to be asked will not be "if it is possible" but rather "when it will be possible". The BCIs present today as a technology of high potential, although in embryonic phase.
In my opinion, an example of enormous success in the medical field refers to Locked-In syndrome. There are patients who are permanently bedridden for weeks or months, in the conscious state, but without their body being able to communicate with the outside, only being able to hear and some have sensitivity to the touch. Using the electroencephalography, with the placement of sensors in the patient's head, BCIs allow the reading and identification of thought patterns. Once two of these patterns can be clearly identified, with the help of a computer that processes and classifies the signal, the patient will be able to answer "Yes" or "No" to the questions posed. Even within the limitations described, imagine the happiness of the patient and his family when establishing this communication channel.
Many other solutions are now used in the area of BCIs, such as neuro-prosthetics, treatment of attention disorders, neuro-rehabilitation or simply fun and leisure. Regarding this last example, the MindX game is being developed at the Champalimaud Foundation, with the aim of "flying" between circles that appear on the screen. In order to perform this movement, the player uses only the thought, controlling in a natural way his movements. Contrary to other systems presented a little around the world, this solution developed by a team of portuguese neuroscientists does not require days of training. Its use is practically immediate, representing a huge advance and potential of applicability on new scenarios.
It is very important to understand the structure and all synapses responsible for the electrical impulses inside our brain. Make a trip through the interior of the brain, from somatosensory cortex to white matter:
SMART Health IT is an open based technology platform that enables innovators to create apps that seamlessly and securely run across the healthcare system. Using an electronic health record (EHR) system or data warehouse that supports the SMART standard, patients, doctors, and healthcare practitioners can draw on this library of apps to improve clinical care, research, and public health.
The SMART App Gallery provides a central place to connect apps and app users, offering developers a free and open way to share their apps and healthcare providers an easy way to find and try SMART compatible apps.
Facebook has more than 2500 career openings including some jobs related to Brain-Computer Interfaces.
As rumors pile up that Facebook is working on moving into consumer hardware, all eyes remain on the company’s Building 8 division, which handles those projects. Dugan presented onstage at Facebook’s F8 conference this year, talking about some of the company’s more long-shot projects, like embeddable brain sensors that could promote a more tight interface with hardware products.
The International Annual BCI-Research Award, endowed with 6,000 USD, is one of the top accolades in BCI research. The Award was created to recognize outstanding and innovative research in the field of Brain-Computer Interfaces. The Award is open to any brain-computer interface research worldwide and 12 projects are nominated before the winner is announced.
The 12 nominated projects will be invited to submit a chapter to be published in Springer
The winner gets 3,000 USD, the 2nd place 2,000 USD and the 3rd place 1,000 USD
The BCI-award is donated by g.tec, a leading provider of BCI research equipment
The competition is open to any BCI group worldwide. .