Parametric Modelling of EEG Data for the Identification of Mental Tasks

Abstract

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task. When a person performs specific events, such as cued imagery tasks, left-hand or right-hand movements, imagined motor tasks and auditory tasks, corresponding variations in the characteristics of the person’s EEG signal take place. These are typically identified by socalled event-related potentials (ERP). For example, event-related potentials associated with real and imagined motor tasks exhibit frequency-specific characteristics: a decrease in EEG band power occurs on the contra-lateral side, a phenomenon known as Event-Related Desynchronization (ERD), followed some time later by an increase in band power on the ipsi-lateral side, known as Event -Related Synchronization (ERS). Hence the detection and identification of ERD and ERS phenomena would enable the classification of mental activity. Such techniques can find useful application in brain-computer interface (BCI) systems where EEG data is measured from the brain and processed by a computer so as to, for example, detect and classify real or imagined left and right-hand movements for the execution of useful tasks such as wheelchair navigation (Pfurtscheller et al., 2006). Several signal processing techniques have been proposed to classify left and right-hand movement from the EEG signal either by detecting ERD and ERS phenomena directly, or by the application of appropriate signal analysis techniques which are characterised by the ERD/ERS phenomena. These include the inter-trial variance approach, the Short-time Fourier Transform, Wavelet Transform methods and Source Localization methods (Pfurtscheller & Lopes da Silva, 1999; Qin et al., 2004). A different approach, on which this chapter will focus, aims to capture the dynamics of the EEG signal by means of auto-regressive (AR) or auto-regressive moving average (ARMA) parametric models (Pardey et al., 1996). This chapter will specifically address the use of such models for the identification and classification of imagined left and right-hand movements. It will start with a literature review on the use of AR and ARMA parametric models for EEG signals and their practical applications. It then proceeds to report, in a unified manner, a number of novel contributions proposed and published by the authors as summarized below.