Decoding natural reach-and-grasp actions from human EEG

Objective. Despite the high number of degrees of freedom of the human hand, most actions
of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study
we attempt to discriminate these three different executed reach-and-grasp actions utilizing
their EEG neural correlates. Approach. In a cue-guided experiment, 15 healthy individuals
were asked to perform these actions using daily life objects. We recorded 72 trials for each
reach-and-grasp condition and from a no-movement condition. Main results. Using low-
frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies
of 72.4%, STD ± 5.8% between grasp types, for grasps versus no-movement condition peak
performances of 93.5%, STD ± 4.6% could be reached. In an offline multiclass classification
scenario which incorporated not only all reach-and-grasp actions but also the no-movement
condition, the highest performance could be reached using a window of 1000 ms for feature
extraction. Classification performance peaked at 65.9%, STD ± 8.1%. Underlying neural
correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed
significant differences starting from approximately 800 ms to 1200 ms after the movement onset
which is also the same time frame where classification performance reached its maximum.
Significance. We could show that it is possible to discriminate three executed reach-and-
grasp actions prominent in people’s everyday use from non-invasive EEG. Underlying neural
correlates showed significant differences between all tested conditions. These findings will
eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive
way, which could ultimately benefit motor impaired end users in their daily life actions.
Keywords: reach-and-grasp decoding, EEG, grasp neural correlates, grasp motor decoding,
motor-related cortical potential, movement-related cortical potential