Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) have been proposed
as a way to restore and replace motor function or communication in severely paralyzed
people. To date, most motor-based BCIs have either focused on the sensorimotor cortex
as a whole or on the primary motor cortex (M1) as a source of signals for this purpose.
Still, target areas for BCI are not confined to M1, and more brain regions may provide
suitable BCI control signals. A logical candidate is the primary somatosensory cortex
(S1), which not only shares similar somatotopic organization to M1, but also has been
suggested to have a role beyond sensory feedback during movement execution. Here,
we investigated whether four complex hand gestures, taken from the American sign language
alphabet, can be decoded exclusively from S1 using both spatial and temporal information.
For decoding, we used the signal recorded from a small patch of cortex with subdural
high-density (HD) grids in five patients with intractable epilepsy. Notably, we introduce
a new method of trial alignment based on the increase of the electrophysiological
response, which virtually eliminates the confounding effects of systematic and non-systematic
temporal differences within and between gestures execution. Results show that S1 classification
scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex
as a whole (85%), and significantly above chance level (25%). We conclude that S1
offers characteristic spatiotemporal neuronal activation patterns that are discriminative
between gestures, and that it is possible to decode gestures with high accuracy from
a very small patch of cortex using subdurally implanted HD grids. The feasibility
of decoding hand gestures using HD-ECoG grids encourages further investigation of
implantable BCI systems for direct interaction between the brain and external devices
with multiple degrees of freedom.