Software platform for managing the classification of error- related potentials of observers

International Conference on Bio-Medical Instrumentation and related Engineering and Physical Sciences

物理学;医药卫生

Asvestas, P.^1 ; Ventouras, E.-C.^1 ; Kostopoulos, S.^1 ; Sidiropoulos, K.^1 ; Korfiatis, V.^2 ; Korda, A.^2 ; Uzunolglu, A.^2 ; Karanasiou, I.^2,3 ; Kalatzis, I.^1 ; Matsopoulos, G.^2

Department of Biomedical Engineering, Technological Educational Institution of Athens, Athens, Greece^1

School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece^2

Institute of Communications and Computer Systems, Athens, Greece^3

关键词: Development tools;    Error observation;    Error related potentials;    Feature selection algorithm;    Integrated platform;    K-nearest neighbours;    Software platforms;    Software systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/637/1/012033/pdf
DOI  :  10.1088/1742-6596/637/1/012033

学科分类：卫生学

来源: IOP

【 摘 要 】

Human learning is partly based on observation. Electroencephalographic recordings of subjects who perform acts (actors) or observe actors (observers), contain a negative waveform in the Evoked Potentials (EPs) of the actors that commit errors and of observers who observe the error-committing actors. This waveform is called the Error-Related Negativity (ERN). Its detection has applications in the context of Brain-Computer Interfaces. The present work describes a software system developed for managing EPs of observers, with the aim of classifying them into observations of either correct or incorrect actions. It consists of an integrated platform for the storage, management, processing and classification of EPs recorded during error-observation experiments. The system was developed using C# and the following development tools and frameworks: MySQL, .NET Framework, Entity Framework and Emgu CV, for interfacing with the machine learning library of OpenCV. Up to six features can be computed per EP recording per electrode. The user can select among various feature selection algorithms and then proceed to train one of three types of classifiers: Artificial Neural Networks, Support Vector Machines, k-nearest neighbour. Next the classifier can be used for classifying any EP curve that has been inputted to the database.