Neuroscience, tCS and EEG blog

Trends in tDCS at SfN 2014

Posted by Stephen Dunne on Dec 4, 2014 12:53:00 PM

This year at SfN we had 20 posters and 2 nano-symposia featuring tDCS in the title from a similar number of labs. On the face of it this is very good for the field that so many labs are exploring the technology but I think it’s interesting that only 3 directly address the parameters of stimulation. Truing et al. looked at optimisation in general through modelling, Kim et al. looked at electrode position and Murray et al. looked at current intensity. The rest (or at least some of them) essentially apply some “standard” protocol. 

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10% of the brain myth ... discredited

Posted by Alejandro Riera on Nov 24, 2014 3:49:13 PM

There are some urban legends, myths, memes, lies or whatever you want to call it ... that are so much given for granted by certain people that it really amazes me. One of them is the so called 10% of the brain myth:

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Topics: Brain, Science, basic neuroscience

How the visual system manages binocular rivalry

Posted by Oscar Ripollès on Nov 15, 2014 1:28:00 PM

The visual system is a complex organization that helps us behave successfully in a world full of stimuli. The visual system is unable to measure the properties that define the physical world. What we see is just our subjective perception of the world. Consequently, we can say that we don’t see the world the way it really is, but as a mixture of our sensory input and our past experiences.

A challenge we face in our perception of images is that we cannot specify the objects and conditions in the real world that create our sensory inputs. That is, we cannot resolve the “inverse problem”. From an evolutionary point of view, our visual system has evolved to maximize “reproductive success” when interpreting the physical world. In this sense, evolution of the human species can explain why our visual system had to improve and manage correctly light, color, depth, etc. Humans and other animals needed better information to find food or detect predators that could pose a threat.

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Biological Psychiatry

Posted by Aureli Soria Frisch on Nov 3, 2014 9:44:31 AM

Today I would like to relate Machine Learning to different concepts like biological psychiatry, stratified medicine, or the “subjectivity gap between neuroscience research and the clinical reality for patients with mental disorders”. Behind all these concepts there is a redefinition of the diagnostic process, which should incorporate more and more objective data. Although this constitutes a trend in mental health, it is the basis for a paradigm change that has embraced all branches of medicine already for some time. This paradigm change enables as well the application of Machine Learning in health applications. Biological psychiatry, and especially stratified medicine imply a change in the type of Machine Learning / Computational Intelligence methodology to be used in the medical domain.

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Designing Games for ADHD Neurofeedback Training

Posted by David Ibañez on Oct 21, 2014 9:29:00 AM

ADHD neurofeedback training aims to present impulsivity and hyperactivity features measured in the brain activity in real time to a child suffering from Attention Deficit Hyperactivity Disorder (ADHD) for him/her to learn how to self-regulate them. As was discussed in previous posts this technique has turned out to be a good complement to medication inducing enduring effects. Obviously one of the key points of neurofeedback training is the calculation of robust reliable attention and impulsivity features that will be trained by the child. The other important aspect is the game itself, that is nothing less than the means by which the child interacts with the neurofeedback application. 

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Synchronizing your EEG recordings

Posted by Javier Acedo on Oct 16, 2014 11:15:30 AM

So you’ve got tons of mega bytes of EEG data from a big recording campaign with several volunteers. You persuaded them to wear your EEG recording device for 40 minutes, performing several mental tasks and receiving all types of external stimuli. You are now ready to process the data and confirm your breakthrough hypothesis.

Well, yes, but did you invest effort in having the appropriated synchronization system for your recordings? If the answer is no you might end just having a bunch of disorganized EEG data where it would be impossible to know when the subjects were performing each of the mental tasks or when the stimuli were presented to them. Your breakthrough then would have to wait to the next recording campaign.

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Face detection libraries: A qualitative evaluation

Posted by Javier Marin on Oct 6, 2014 9:35:35 AM

Neurokai's services offer different solutions for industrial and academic researchers. Our Experience Lab service focuses on providing a custom solution for emotion recognition based on a multimodal system (EEG, ECG, GSR and facial). In order to develop our vision-based emotion recognition system, integrated within the multimodal one, we analysed several libraries for face detection. The main objective of our research at that point was to find a robust computer vision library to be integrated in our multimodal emotion recognition system, which includes vision as well as electrophysiological modalities. In this post we will share with you what computer vision libraries we found in the community and how the methods performed, when it was possible, after running them on our own video recordings. It is worth mentioning that in our videos people were recorded wearing the Enobio cap and the videos were performed with low lighting conditions, which made the face detection task slightly more difficult.

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Topics: Neuroscience, eeg, Brain, Neuro

SSVEP-Based Brain Conputer Interfaces

Posted by David Ibañez on Sep 22, 2014 9:53:00 AM

As I explained in one of my previous posts, a Brain Computer Interface (BCI) is a direct communication pathway connecting the brain to a computer or other external device. BCIs do not depend on the brain’s normal action pathways through peripheral nerves and muscles. This makes them the ideal technology to develop systems assisting or repairing human cognitive or sensory-motor function. There are a varieity of different BCI modalities although the most commonly used are motor imagery, P300 and steady state visual evoked potentials. In this post I’d like to focus on SSVEP describing SSVEPs themselves, SSVEP-based BCI systems and presenting the most commonly used SSVEP detection methodology. SSVEP-based BCIs offer two main advantages over other BCIs: they have a larger information transfer rate, and they require a shorter calibration time.

Let’s start introducing what SSVEPs are. Steady state visual evoked potentials are a resonance phenomenon occurring in the brain that can be measured in the EEG. It occurs when a person is focusing his/her visual attention on a flickering light source. When SSVEP is elicited, oscillatory components can be observed in the EEG that match the stimulation frequency and its harmonics. This effect is mainly observed in the visual cortex, the part of the cerebral cortex responsible for processing visual information, shown in the next figure.

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Topics: SSVEP

Brain-to-brain communication

Posted by Giulio Ruffini on Sep 10, 2014 5:44:00 PM

Some would say that my mom is a bit eccentric … When we were little, she would have us play a fun game ... well, I remember this happening at least once probably around 1975. The game was … telepathy. My siblings and I would sit around for a while trying to transmit each other thoughts. Could we transmit words to each other? Concentrate hard and … 

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Topics: brain computer interface, brain stimulation, affective computing, experiment, eeg techniques, basics of eeg, databases, basic neuroscience, liveness detector, neuromarketing

On the importance of negative results

Posted by Anton Albajes-Eizagirre on Sep 1, 2014 10:51:08 AM

This blog has included a fair amount of posts about research studies, as well as about tools, procedures and tips. With all this content, we intend to give you all the ingredients to enter the fascinating world of research and applications of applied neuroscience. 
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Topics: brain computer interface, applications of eeg, brain stimulation, basic neuroscience