EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement

Abstract

Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase.

EEG-based brain–computer interfaces

“…Recent interest in network models of CNS function has prompted studies exploring the use of phase information. Indeed, the success of the surface Laplacian for amplitude-based features may be due to inclusion of phase effects [40]. Phase effects may also be involved in amplitude-based common spatial patterns and other data-driven spatial filtering methods [41].…”

Networks-based brain computer interfaces for decoding neurophysiological dynamics

“…is result of the convolution with the Gabor wavelet and is used to extract the instantaneous phase of the signal. An alternative way that is frequently used to extract the phase of the narrowband signal is by using the Hilbert Transform [131,132].…”

“…Such phase-based features are typically used to study synchrony between di↵erent parts of the brain, called functional connectivity[124,127,130]. In some studies, they have been used as feature extraction methods to classify motor imagery of di↵erent limbs and actual movement of hands versus rest[123,132,153]. Moreover, some studies also show that a combination of amplitude and phase-based features perform better than either type of features leveraging on the fact that both methods essentially estimate di↵erent types of neural activity and their respective influence on the measured brain signals[123,154,155].…”

“…Those pairs of signals which maintain a near constant phase-di↵erence with respect to each other are said to be synchronized in phase and thus functionally related; while those whose phase-di↵erence varies randomly with respect to each other are said to be functionally unrelated. However, unlike other estimators of phase-synchrony, PLVs do not discriminate between zero and nonzero phase-di↵erence but only estimates the consistency of the phase-di↵erence.Depending on the hypothesis in question, a researcher may choose to select those functional networks that contain either or both zero and non-zero phase-di↵erence between pairs of signals and these functional connections have been said to reveal di↵erent types of underlying neurophysiological properties[132]. However, for testing the hypothesis in this chapter, it is only required to identify discriminative transient phase-based functional connections and there is no basis to choose a particular value of phase-di↵erence.…”

“…However, for testing the hypothesis in this chapter, it is only required to identify discriminative transient phase-based functional connections and there is no basis to choose a particular value of phase-di↵erence. Rather, biasing the estimator towards a particular value of phase-di↵erence may cause the feature extraction algorithm to miss out on certain important and discriminative connections for di↵erent movements[132]. This may be one of the reasons why PLVs have been used for classification of di↵erent motor activity[123,145,156].…”