Spell to exorcise the devil in Latin

Date: Aug 19, 2020

Exorcizo te, immundissime spiritus, omnis incursio adversarii, omne phantasma, omnis legio, in nomine Domini nostri Jesu Christi eradicare, et effugare ab hoc plasmate Dei. Ipse tibi imperat, qui te de supernis caelorum in inferiora terrae demergi praecepit. Ipse tibi imperat, qui mari, ventis, et tempestatibus impersvit. Audi ergo, et time, satana, inimice fidei, hostis generis humani, mortis adductor, vitae raptor, justitiae declinator, malorum radix, fomes vitiorum, seductor hominum, proditor gentium, incitator invidiae, origo avaritiae, causa discordiae, excitator dolorum: quid stas, et resistis, cum scias. Christum Dominum vias tuas perdere? Illum metue, qui in Isaac immolatus est, in joseph venumdatus, in sgno occisus, in homine cruci- fixus, deinde inferni triumphator fuit. Sequentes cruces fiant in fronte obsessi. Recede ergo in nomine Patris et Filii, et Spiritus Sancti: da locum Spiritui Sancto, per hoc signum sanctae Cruci Jesu Christi Domini nostri: Qui cum Patre et eodem Spiritu Sancto vivit et regnat Deus, Per omnia saecula saeculorum. Et cum spiritu tuo. Amen.

Developed a system to create smarter brain-computer interfaces

Date: Aug 19, 2020

Experts from Skoltech, INRIA and the RIKEN Advanced Intelligence Project investigated the capabilities of several modern machine learning algorithms to solve the determination of mental load and affective states of a person. The developed software can be used to create smarter brain-computer interfaces (BCIs), which can be used in medicine and other fields.

The research results are published in the journal IEEE Systems, Man, and Cybernetics. A BCI provides a link between the human brain and a computer, allowing a person to control various devices, such as a robot arm or a wheelchair, based on a signal from the brain (active BCI).

BCI also allows you to track and classify psycho-emotional states of a person (passive BCI). The signals from the brain to the BCI are usually measured using electroencephalography (EEG), a common non-invasive method for measuring the electrical activity of the brain.

The "raw" data obtained as a result of EEG in the form of continuous signals must undergo sufficiently thorough processing before they can provide an accurate determination of the mental load and affective states of a person, which is a prerequisite for the correct operation of a passive BCI.

The experimental data available to date indicate that the accuracy of these measurements is insufficient even for solving such simple problems as determining the difference between high and low mental workload, not to mention their use in practical applications.

“This low measurement accuracy is due to the extremely complex structure of the human brain. Imagine that our brain is a huge orchestra, in which thousands of instruments participate, and we need to use a limited number of microphones and sensors to highlight the characteristic sound of each individual instrument. ”

noted one of the authors of the article, a professor at the Skoltech Center for Scientific and Computing Engineering for Big Data Problems (CDISE) Andrzej Chychocki.

It follows from this that more reliable and accurate algorithms are required to solve the problems of classifying EEG data and recognizing various patterns of the brain. Professor Chikhotskiy and his colleagues examined two groups of machine learning algorithms, Riemannian geometry classifiers (RGC) and convolutional neural networks (CNN), which performed well in active BCIs.

The researchers decided to find out whether these algorithms will cope not only with the so-called imaginary motor tasks, in which the subject imagines certain movements of the limbs, without actually performing them, but also with the tasks of assessing mental load and affective states.

Scientists have held a kind of "competition" for seven algorithms, two of which scientists have developed independently by optimizing the well-proven Riemannian algorithms. In one of the two experiments, a typical BCI scheme was used, in which the algorithms were first trained on data about a particular subject, and then tested on that subject.

The second experiment was conducted without reference to a specific subject, and this scheme is much more complicated, since the brain activity can be very different for different people. The experiments used real EEG data from earlier experiments by one of the authors of the article Fabien Lotte and his colleagues, as well as the DEAP database, which collected data on the analysis of human emotional states. Scientists found that the deep neural network bypassed all of its "competitors" in solving the problem of assessing mental load, but at the same time it did a poor job of classifying emotional states, but two algorithms with Riemannian optimization performed well in solving both problems.

In the article, the authors conclude that it is much more difficult to use a passive BCI for classifying affective states than for assessing mental load, and the calibration of the algorithm without reference to a specific subject still gives significantly lower accuracy.

“In the next stages of the research, we plan to use more sophisticated methods based on artificial intelligence (AI) and, first of all, methods of deep learning, with which it is possible to detect the smallest changes in signals and patterns of the brain. Deep neural networks can be trained on large datasets containing information about a large number of subjects, different test scenarios, and test conditions. Artificial intelligence, the creation of which has become a real revolution, can be very useful for BCI and solving problems of recognizing human emotions "

- said Chikhotsky.