Exploring neuro-muscular synergies of reaching movements with unified independent component analysis

The coordinated recruitment of group of muscles through muscles synergies is known to simplify the control of movements. However, how and to what extent such control scheme is encoded at a cortical level is poorly understood. So far, electroencephalography (EEG) and electromyography (EMG) have been used, separately, to investigate the cortical regions of the human brain which may be involved in activating muscle synergies. Here we aim at extending these results by looking for a hierarchical relationship between cortical and muscular sources of activity (neuro-muscular synergies) with a unified analysis of independent components (IC) simultaneously extracted from both EEG and EMG signals. We show for the first time how the direct fusion of EEG and EMG signals to extract unified ICs (unICs) can overcome the limitations of previous approaches, i.e., the difficulty in linking neural with muscular activations, and the lack of reliability of separate preprocessing techniques. Our results show that unified ICs were physiologically meaningful components in agreement with previous works. UNICA (Unified Independent Component Analysis) can also be considered as a solution for estimating overcomplete ICA on EEG and EMG data. These findings are an important step towards an understanding of the cortical control of human muscles synergies, and may have important applications for understanding movement dysfunction and to develop novel approaches for brain-computer interfaces and neuroprostheses.