A Multivariate Auto-Regressive Method to Estimate Cortico-Muscular Coherence for the Detection of Movement Intent | Zendy

Giacomo Severini | Zendy; Silvia Conforto | Zendy; Maurizio Schmid | Zendy; Tommaso D’Alessio | Zendy

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A Multivariate Auto-Regressive Method to Estimate Cortico-Muscular Coherence for the Detection of Movement Intent

Author(s) -

Giacomo Severini,

Silvia Conforto,

Maurizio Schmid,

Tommaso D’Alessio

Publication year - 2012

Publication title -

applied bionics and biomechanics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.397

H-Index - 23

eISSN - 1754-2103

pISSN - 1176-2322

DOI - 10.1155/2012/353272

Subject(s) - coherence (philosophical gambling strategy) , anticipation (artificial intelligence) , computer science , electroencephalography , movement (music) , multivariate statistics , electromyography , brain–computer interface , artificial intelligence , speech recognition , physical medicine and rehabilitation , pattern recognition (psychology) , psychology , machine learning , mathematics , statistics , neuroscience , medicine , acoustics , physics

In this work a time-frequency approach to estimate the Cortico-Muscular Coherence for the detection of the movement intent is presented, assessed on simulated data, and evaluated experimentally during different motor tasks performed by healthy subjects and patients suffering from different types of tremor. Cortico-Muscular Coherence is an index of the coupling of EEG signal in the cortical area with sEMG activity in the frequency domain, and its contributions in the beta band (15–30 Hz) have been associated to the movement intent. Cortico-Muscular Coherence estimation is here achieved by considering a closed-loop representation of the signals under analysis obtained through Multivariate Auto Regressive modeling. Significance levels for Cortico-Muscular Coherence are assessed by means of a surrogate data analysis approach. The detection technique is able to reveal significant Cortico-Muscular Coherence changes in 79% of the experimental trials, with a mean anticipation of 1.35 s with respect to movement onset. Time-frequency estimation of Cortico-Muscular Coherence can provide an insight for the development of a multimodal BCI able to integrate information from the brain activity in the functioning of assistive devices.

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