NEUROTECH CUP 2020 инженерная секция

Currently, more and more remote events are taking place online: Olympiads, exams, tests, etc. Proctoring systems are used to track the honest writing of tests. However, such systems have a number of flaws. For example, a student can put a second computer next to it or put an assistant outside the range of the camera, and hide an earpiece under his loose hair! Is it possible with the help of modern neurotechnologies and artificial intelligence technologies to ensure the honest writing of the test paper when connected remotely?

Develop a technical system using artificial intelligence and neurotechnology technologies to ensure more honest test writing.

You need to provide a report that contains:

1. Description of existing proctoring solutions, their advantages and disadvantages;
2. Description of technologies (in particular neurotechnologies and artificial intelligence technologies) that are used in the system you offer;
3. Description of the parameters and data that the developed system registers;
4. How are these parameters and data related to the fair writing of the checklist?
5. Block diagram of the proposed system and / or prototype of the developed system;
6. Information on approbation of the developed system.

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

During intellectual activity, for example, playing chess, a person experiences stress, which has a direct impact on the quality of the chess player's play. For example, with a lack of time, a person may "yawn" a piece, or make a mistake during a long complex game. Evaluate how much stress a chess player experiences when playing chess and how this affects the quality of the game.

Conduct research on the effect of stress on the quality of a chess player's play. To do this, develop a system for assessing the stress of a chess player while playing chess.

As a report it is necessary:

1. Describe the parameters, the value of which can influence the stress level of the chess player. Justify your answer.
2. Describe technologies (in particular, neurotechnology and artificial intelligence) that can be used to assess the level of stress.
3. Conduct an experiment and video the game of chess players and their stress level during the game.
4. Assess the impact of stress levels on the quality of a chess player's play.
5. Describe how (if possible), the chess player could use this research to improve his playing quality?

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

PC control is one of the popular applications for HMI. It is of particular relevance for the rehabilitation of people who have limited or no ability to control a PC in the usual ways: keyboard, mouse.

One of the HMI options that would allow such people to be controlled is the HMI based on the registration and processing of electrical muscle signals - electromyogram. There are various implementations of such an HMI. You are invited to independently implement such a system and demonstrate its performance by the example of controlling a computer game.

It is necessary to develop an EMG interface for PC control. It is necessary to demonstrate its practical applicability by the example of controlling a computer game and / or typing from an on-screen keyboard.

As a report, you must provide:

1. Description of the hardware part of the interface: provide a block diagram of the device, justification for the choice of components. Separately, it is worth considering the convenience and safety of the system by the user;
2. Description of the software part of the interface: block diagram of signal processing algorithms and software, source code of the program and (if any) executable files;
3. Video recording (as a single duplicate) of the demonstration of the system's operation on the user, from which it should be obvious how the system connects to the user, how the program is launched, control in a computer game and / or typing.

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

Separately attach diagrams / drawings of the HMI hardware and the source commented software code.

Video recording of the demonstration.

The Myo Armband: The Future of Gesture Control
Development of Wireless EMG control system for rehabilitation devices

Assessment of the perception of information is an urgent task in those areas when it is necessary to understand the level of interest of the user in the information received by him, the degree of his interest. For example, this may be relevant in such areas as pedagogy (interest of listeners in a lecture, lesson), psychology (conducting experimental work), advertising (interest of a potential consumer).

Analysis of human biosignals can be used to assess the perception of information, because there is a relationship between the level of interest of a person, his concentration on certain biosignals. Within the framework of this case, it is proposed to build a physiological model and installation that will allow such an analysis to be carried out.

It is necessary to develop a system that allows assessing the level of perception of information by the user in real time. Biosignals, video, etc. can be used as registered signals. It is also necessary to come up with an experiment that clearly demonstrates the correctness of the developed system.

As a report, you must provide:

1. Justification of the choice of signals and description of the physiological model that underlie the system being developed;
2. Technical description of the hardware part: rationale for the choice of sensors and their characteristics, installation design (block diagram of the device, schematic diagrams, drawings, layout, fastening elements, etc.);
3. Technical description of the software part: block diagram of signal processing algorithms and software, source code of the program and (if any) executable files;
4. Description of the experiment with justification of its correctness and objectivity (from the point of view of physiology), which will be used to demonstrate the operation of the device;
5. Video recording of the experiment (as a single duplicate), from which it should be obvious how the system starts and works.

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

Separately attach diagrams / drawings of the HMI hardware and the source commented software code.

Video recording of the demonstration.

A method for assessing the perception of information
Neurolaboratory

Biometric identification systems have become a fairly dense part of our life: systems based on fingerprint reading, facial analysis, iris, etc. have become quite widespread.

Also, identification can be carried out based on the analysis of human biosignals (for example, an electrocardiogram signal, an electroencephalogram). As devices for analyzing such signals become cheaper and miniaturized, they can also become widespread.

It is necessary to develop a user identification system. The choice of the biometric data used is made by the participants independently, their number is not limited and they can be used in combination. As a result of the work, it is necessary to demonstrate the system in action by the example of the possibility of identifying at least 3 people. It is also necessary to evaluate (for example, experimentally) the probability of successful user identification (the number of attempts to select and justify - independently).

As a report, you must provide:

1. Justification of the choice of biosignals and a description of the principles of identification based on them;
2. Hardware technical description: selection of sensors, installation design (block diagram of the device, schematic diagrams, drawings, layout, fastening elements, etc.);
3. Technical description of the software part: block diagram of signal processing algorithms and software, source code of the program and (if any) executable files;
4. Description of the experiment with justification of its correctness and representativeness, which will allow assessing the probability of correct user identification;
5. Video recording of the experiment (as a single duplicate), from which it should be obvious how the system starts up and identifies users.

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

Separately attach diagrams / drawings of the HMI hardware and the source commented software code.

Video recording of the demonstration.

What is biometrics?
Modern biometric identification methods
R.M. Ball, J.H. Connel, Sh. Pancanti, "A Guide to Biometrics"
ECG-based biometric identification: some modern approaches
User identification based on electroencephalography using "Brain-computer interface" technologies

Modern limb prostheses are quite complex technical devices, an important component of which is the control system. The ease of use, safety and reliability of operation depend on the quality of its work.

Control systems based on processing electromyographic signals from the muscles of the prosthesis wearer (for example, Bebionic) are widely used. In most cases, this is a trigger system that allows you to detect the presence / absence of muscle tension and form one predetermined gesture, the choice of which is carried out, for example, with a switch.

The development of signal processing methods and an increase in the performance of small-sized computing modules make the issue of developing a system that would recognize the gesture reproduced by the user and form a certain gesture corresponding to it on the prosthesis is urgent.

It is necessary to develop a model of an upper limb prosthesis control system based on the reading and processing of two electromyogram (EMG) channels. You need to independently determine the best place for recording EMG signals on the user's hand, develop a device for reading electromyogram signals (if necessary), and also write software to process these signals. Other biosignals (for example, EEG) can also be used for control (optional). Demonstration of the system operation can be carried out on a mechanical model of the hand (for example, used in kits from BiTronics Lab or similar): the number of stably reproduced gestures, processing of erroneous actions, architecture and implementation of the software part will be evaluated.

As a report, you must provide:

1. Description of recognition algorithms for gestures reproduced by the user;
2. Description of the software part of the project: block diagram of signal processing algorithms and software, source code of the program and (if any) executable files;
3. Description of the technical component of the project: the equipment used, the location and justification for the choice of the place for fixing the EMG electrodes, a block diagram describing the interaction of hardware units with each other, other materials (schematic diagrams, drawings, layout, fastening elements, etc.);
4. Description of the experiment that will assess the correctness of the device. In particular, the number of recognizable gestures should be indicated;
5. Video recording of the experiment (as a single duplicate), from which it should be obvious that the system actually implements the functionality stated in clause 4.

Presentation in pdf format, containing no more than 10 slides and a text report in .doc or .docx format, the volume of the report - no more than four A4 pages, font - Arial, font size - 12 pt.

Separately attach diagrams / drawings of the HMI hardware and the source commented software code.

Video recording of the demonstration.

Bebionic
Catalog of hand prostheses
Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network
Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review
Surface EMG Classification for Controlling a Prosthetic Hand