EEG signal phase analysis for brain-computer interfacing

Abstract

Brain-computer interfaces (BCIs) are devices that provide a direct link between an individual's brain and a computer, thereby allowing the user to communicate with the surrounding environment or to control equipment solely with the use of his mental activity. The application possibilities for BCIs are wide, ranging from communication devices for locked-in patients to brain-controlled gaming tools. The potential uses of BCI systems have led to the establishment of various BCI research groups worldwide working on different aspects to improve the performance and reliability of BCI systems. Most of the developed systems rely on electroencephalography (EEG) as a modality for recording brain activity. This choice is understandable since when compared to other recording modalities, EEG provides a non-invasive, and affordable solution for recording the dynamic electrical activity in the brain. A central issue that determines the performance of EEG-based BCIs is the extraction of reliable features that can adequately represent phenomena in the underlying brain activity and that can be used to distinguish between different mental states. Several feature extraction methods have been developed for this purpose. However, the majority of these methods focus primarily on the amplitude and power characteristics of the EEG signals. On the other hand, the phase relationships between different regions of the brain, which are associated with interactions between different neuronal areas in the brain have been considered to a much lesser extent for task discrimination in BCIs. In this work, two novel feature extraction methods that consider explicit phase information in the EEG data, namely the 'phase-synchronisation'-based common spatial patterns (P-CSP) method and the analytic common spatial patterns (ACSP) method, are proposed. The P-CSP method considers the most discriminative phase synchronisation links in order to separate two classes of data, while the ACSP method considers an analytic representation of the EEG signals to obtain an explicit representation of amplitude and phase components of the EEG signals. The performance of the two methods is analysed through a number of simulation examples and tests on real EEG data, where it is shown that the methods can yield a good classification rate and additionally provide informative spatial patterns on the underlying discriminative phenomena for the considered tasks. Furthermore, the ACSP method is also tested on a six-target phase coded steady state visual evoked potential (SSVEP) setup, where the classification accuracy depends primarily on the discrimination of brain activity in response to visual targets that only differ in their phase. It is shown that the ACSP method outperforms the conventional CSP method and other techniques typically used for such setups, and gives spatial patterns that are more representative of the underlying activity than the conventional CSP technique.