EEG PATTERN RECOGNITION: Application to a Real Time Control System for Android-Based Mobile Devices

摘要

This paper describes a new EEG pattern recognition methodology in Brain Computer Interface (BCI) field. The EEG signal is analyzed in real time looking for detection of "intents of movement". The signal is processed at specific segments in order to classify mental tasks then a message is formulated and sent to a mobile device to execute a command. The signal analysis is carried out through eight frequency bands within the range of 0 to 32 Hz. A feature vector is conformed using histograms of gradients according to 4 orientations, subsequently the features feed a Gaussian classifier. Our methodology was tested using BCI Competition IV data sets I. For "intents of movements" we detect up to 95% with 0.2 associated noise, with mental task differentiation around 99%. This methodology has been tested building a prototype using an Android based mobile telephone and data gathered with an EPOC Emotive headset, showing very promising results.