Towards a highly accurate mental activity detection bay electroencephalography sensor networks

Abstract

Consulta en la Biblioteca ETSI Industriales (8956)

[EN] The possibility to detect reliably human brain signals by small sensors can have substantial impact in healthcare, training, and rehabilitation. This Master the- sis studies Electroencephalography (EEG) wireless sensors, and the properties of their signals. The main goal is to investigate the problem of data interpre- tation accuracy. The measurements provided by small wireless EEG sensors show high variability and high noises, which makes it di_cult to interpret the brain signals. The analysis is further exacerbated by the di_culty in statistical modeling of these signals. This work presents an attempt to a simple statistical modeling of brain signals. Then, based on such a modeling, an optimal data fusion rule of sensors readings is proposed so to reach a high accuracy in the signal's interpretation. An experimental implementation of the data fusion by real EEG wireless sensors is developed. The experimental results show that the fusion rule provides an error probability of nearly 25% in detecting correctly brain signals. It is concluded that substantial improvements have still to be done to understand the statistical properties of signals and develop optimal decision rules for the detection.