Haptic_engagement_eeg

Robotic rehabilitation has attracted a lot of attention as an alternative to expensive hospital-centric procedures. However, robotic-rehabilitation procedures (haptic controls) are shown not to be superior to physical therapy unless there is an active engagement from the patients. This study investigates the effect of haptic control strategies on a subject's mental engagement during a fine motor handwriting rehabilitation task. The considered control strategies include an error-reduction (ER) and an error-augmentation (EA) control which is tested on both dominant and non-dominant hand. During the fine motor task, the mental engagement level is extracted using non-invasive electroencephalogram (EEG). Mental engagement is calculated using the power of theta, alpha, beta frequency bands from EEG. Statistical analysis of the effect of the control strategy on mental engagement revealed that the choice of the haptic control strategy has a significant effect (p $<$ 0.01) on mental engagement depending on the type of hand (dominant or non-dominant). Among the evaluated strategies, EA is shown to be more mentally engaging when compared with ER under the non-dominant hand. Under the dominant hand, both EA and ER evoked distraction rather than engagement.

Documentation https://hemumanju.github.io/haptic-engagement-eeg/

Project based on the cookiecutter data science project template. #cookiecutterdatascience