Decoding motor-adaptation performance from pre- and postmovement EEG signals

Rationale:
Motor adaptation refers to the process of gradually adjusting locomotor patterns via (visual) error feedback to optimize movements. Motor adaptation reflects the plasticity of the sensorimotor system in the brain to update its internal representations of movements to refine motor performance. In the electroencephalography (EEG) signal, beta band activity (13-30Hz) reflects sensorimotor processing in the cortex. However, previous work on the role of sleep in the adaptation of motor skills have yielded contradictory results.

Material and Method:
In this study, we employed a novel fine-motor task, i.e. typing on a mirrored keyboard, to disentagle the role of sleep in motor adaptation. To this end, we trained human experts in touch-typing on the regular keyboard, to type on a mirrored keyboard, and measured their performance before and after a retention interval of either a full night (~8h) of sleep with polysomnography (PSG) or a similar period of wakefulness. In total, we recruited 33 participants (N=16 for the sleep group and N=17 for the wake group). Participants had to type eighteen five-letter, German words on a regular as well as a mirrored keyboard as rapidly and accurately as possible. We trained a linear discriminant analysis (LDA) classifier to decode i) correct from incorrect trials as well as ii) Regular from mirrored-typing trials based on pre- as well as post-movement EEG activity.
Results and Conclusion:
We found that brain activity in the one-second pre-movement interval predicts the outcome of the motor adaptation task. The decoding accuracy increased following a period of sleep but not wakefulness. These results suggest a role for sleep in optimizing motor adaptation processes.