# Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy

## TL;DR

This paper explores how different methods for acquiring motor imagery brain signals can improve the accuracy of brain-computer interfaces, especially for people new to using them.

## Contribution

The study introduces a novel acquisition paradigm that improves classification accuracy for naive users in motor imagery-based BCIs.

## Key findings

- The proposed paradigm achieved 97.5% accuracy for naive subjects, outperforming traditional methods.
- Statistical tests confirmed significant differences in classification accuracy between acquisition paradigms.
- Experienced post-stroke users achieved 96.25% accuracy with traditional paradigms.

## Abstract

In recent years, advances in medicine have been evident thanks to technological growth and interdisciplinary research, which has allowed the integration of knowledge, for example, of engineering into medical fields. This integration has generated developments and new methods that can be applied in alternative situations, highlighting, for example, aspects related to post-stroke therapies, Multiple Sclerosis (MS), or Spinal Cord Injury (SCI) treatments. One of the methods that has stood out and is gaining more acceptance every day is Brain–Computer Interfaces (BCIs), through the acquisition and processing of brain electrical activity, researchers, doctors, and scientists manage to transform this activity into control signals. In turn, there are several methods for operating a BCI, this work will focus on motor imagery (MI)-based BCI and three types of acquisition paradigms (traditional arrow, picture, and video), seeking to improve the accuracy in the classification of motor imagination tasks for naive subjects, which correspond to a MI task for both the left and the right hand. A pipeline and methodology were implemented using the CAR+CSP algorithm to extract the features and simple standard and widely used models such as LDA and SVM for classification. The methodology was tested with post-stroke (PS) subject data with BCI experience, obtaining 96.25% accuracy for the best performance, and with the novel paradigm proposed for the naive subjects, 97.5% was obtained. Several statistical tests were carried out in order to find differences between paradigms within the collected data. In conclusion, it was found that the classification accuracy could be improved by using different strategies in the acquisition stage.