A new approach to the design of neurofeedback systems based on using Artificial Intelligence (AI) tools is proposed. The concept of control models of biological neural networks, and the set-up including equipment and software tools developed in IPME RAS in order to implement the proposed concept is described. as well as the AI methods and programs proposed for use.
The main contribution of the paper is the statement of equivalence between the existence of quadratic Lyapunov function for nonlinear Lurie systems with several infinite sector constraints and fulfillment of the circle criterion in Yakubovich's version for absolute stability and instability. A side result is the theorem about S-procedure losslessness (S-theorem) for any number of infinite sector constraints in the complex case. Additionally the relations between two versions of the MIMO circle criterion are established.
Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.
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