Desarrollo de una base de datos para el análisis de señales EEG durante la aritmetica mental

Abstract

In the scientific world there are several investigations based on information stored in specific databases, these contain information that can be images, audios, statistics, signals, etc. From the latter we find databases of ECG (electrocardiographic), EOG (electrocochleographic), EEG (electroencephalographic), EMG (electromyography) signals, among others. However, many of these signals are captured for very specific purposes, which prevents them from being analyzed from points of view other than the capture process. For example, in EEG signals, the capture of the signals differs from the environment to which the patient is submitted or from the type of test that the researchers are performing, making it unfeasible for other types of analysis, given that the signals vary. Many technological and research advances are largely due to the existence of databases, which have made it possible to use and transform important information for the development of applications in the field of biotechnology, diagnosis and research, reducing time and costs of the research process. This research is based on the collection and analysis of electroencephalographic signals and their respective storage in a database, while the patient performs basic arithmetic mental operations, such as subtraction. For this purpose, we will use the signals obtained in the free access database of Physionet, where there is a section of EEG signals obtained from mental arithmetic processes. The process of obtaining information for the EEG database, will consist of finding the most relevant descriptors determined by the analysis of research in EEG signal processing, also having a user interface that will make access much more dynamic for the user and researcher, to obtain important information and characteristics of these signals. The processing and digital analysis of them, will allow us to acquire the data of interest for the proposed research, which will use descriptor analysis to help demonstrate the differences that may exist within the signals studied and with evident changes in frequency and time.