The EEG consists of the summed electrical activities of populations of neurons, with a modest contribution from glial cells. The neurons are excitable cells with characteristic intrinsic electrical properties, and their activity produces electrical and magnetic fields. Electroencephalography measures the electrical fields that reach the surface of the scalp and are produced by the joint contribution of several neuron’s populations with similar spatial orientation. One of the main advantages of EEG is its non-invasive nature. Other brain electrical-field recording techniques such as electrocorticography (ECoG) require surgery to implant an electrode grid on the cortex surface.Read More
Neuroscience, tCS and EEG blog
My post today will deal with the future. I have recently attended a conference by Pankaj Ghemawat on the World 3.0 (well the conference had a more local component as well but I will try to focus on the more general aspects). As far as I understood the 3.0 world is characterized by the mixing of local and global dimensions in our daily lives. Other local experts were invited to give their views on upcoming technological, scientific, economic and societal trends and concepts (a usual exercise at the beginning of the year).Read More
I chose the title of this post as a tribute to John Lennon´s song but with a small twist… Now we have started playing with our minds! The first videogame I played was called Digger, it ran on a Dragon 32(K) computer and was loaded using a cassette. I used to play for hours and a part from the novelty (at that time) it presented, no adult would have though that videogames might improve my cognitive performance.Read More
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.Read More
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.Read More
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:Read More
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.Read More
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.Read More
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.Read More
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.Read More