# Decoding Complex Imagery Hand Gestures

## TL;DR

This paper introduces a novel EEG-based brain-computer interface paradigm using hierarchical spatial patterns and context to improve decoding of complex hand gestures, achieving significantly higher accuracy than chance.

## Contribution

It presents a new motor imagery paradigm that better connects control signals with actual tasks, enhancing BCI performance for complex gesture decoding.

## Key findings

- Achieved 64.5% accuracy in classifying 8 hand gestures.
- Outperformed chance level by more than 5 times.
- Demonstrated effectiveness with 5 participants.

## Abstract

Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy--intended grasp prediction probability--of 64.5\% for 8 different hand gestures, more than 5 times the chance level.