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Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans. A study using intracranial recordings in humans suggests that upcoming hand movements can be predicted by oscillatory brain features of the LFP signal. [ABSTRACT FROM AUTHOR]

Background: Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources., New Method: In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices., Results: Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling., Comparison With Existing Methods: Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices., Conclusions: Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test., (Copyright © 2016 Elsevier B.V. All rights reserved.)