A. Ferreyra-Ramírez

EEG Pattern Recognition: An Efficient Improvement Combination of ERD/ERS/Laterality Features to Create a Self-paced BCI System

In this paper, a new method based on an efficient improvement combination of Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS) and lateral activity of sensorimotor cortex features is presented to analyze both left and right hand motor imagery tasks. Our proposal uses delta, theta, alfa and beta rhythms to BCI system. From the spectral power, an efficient combination of ERD/ERS/laterality features was used. Because electroencephalogram signals are non-stationary type and highly vary over time and frequency, a detailed time-frequency analysis is applied. Features coming from time-frequency analysis, where eight frequency bands ranging from 0 to 32 Hz were chosen. Features vectors are classified by Gaussian classifier and the final performance is evaluated in cross-validation scheme. This novel approach was tested using the BCI competition IV data set 1. The detection of the left and right hand motor imagery task was very good, with a result of \(96.4\,\%\) using BCI-Competition -IV. When comparing results from others competing methods reported in the literature, our approach resulted the best and useful to create a self-paced BCI-system.

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

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.