Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance.

BACKGROUND

Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.

NEW

METHODS

In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.

COMPARISON WITH EXISTING

METHODS

Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.

RESULTS

To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %.

CONCLUSION

This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.