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Brain-computer interfaces (BCIs) have achieved important milestones in recent years, but the major number of breakthroughs in the continuous control of movement have focused on invasive neural interfaces with motor cortex or peripheral nerves. In contrast, non-invasive BCIs have made primarily progress in continuous decoding using event-related data, while the direct decoding of movement command or muscle force from brain data is an open challenge. Multi-modal signals from human cortex, obtained from mobile brain imaging that combines oxygenation and electrical neuronal signals, do not yet exploit their full potential due to the lack of computational techniques able to fuse and decode these hybrid measurements. To stimulate the research community and machine learning techniques closer to the state-of-the-art in artificial intelligence we release herewith a holistic data set of hybrid non-invasive measures for continuous force decoding: the Hybrid Dynamic Grip (HYGRIP) data set. We aim to provide a complete data set, that comprises the target force for the left/right hand, cortical brain signals in form of electroencephalography (EEG) with high temporal resolution and functional near-infrared spectroscopy (fNIRS) that captures in higher spatial resolution a BOLD-like cortical brain response, as well as the muscle activity (EMG) of the grip muscles, the force generated at the grip sensor (force), as well as confounding noise sources, such as breathing and eye movement activity during the task. In total, 14 right-handed subjects performed a uni-manual dynamic grip force task within $25-50\%$ of each hand's maximum voluntary contraction. HYGRIP is intended as a benchmark with two open challenges and research questions for grip-force decoding. First, the exploitation and fusion of data from brain signals spanning very different time-scales, as EEG changes about three orders of magnitude faster than fNIRS. Second, the decoding of whole-brain signals associated with the use of each hand and the extent to which models share features for each hand, or conversely, are different for each hand. Our companion code makes the exploitation of the data readily available and accessible to researchers in the BCI, neurophysiology and machine learning communities. Thus, HYGRIP can serve as a test-bed for the development of BCI decoding algorithms and responses fusing multimodal brain signals. The resulting methods will help understand limitations and opportunities to benefit people in health and indirectly inform similar methods answering the particular needs of people in disease.

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.