# Utilizing statistical analysis for motion imagination classification in brain-computer interface systems

## TL;DR

This paper introduces a new method called FARKA to improve motion imagination classification in brain-computer interface systems using EEG data.

## Contribution

The novel FARKA method combines Riemannian alignment and kernel adaptation for improved inter-individual motion imagination classification.

## Key findings

- FARKA outperforms existing methods in classifying motion imagination tasks using EEG data.
- The method enhances classification performance and efficiency across different individuals.
- Experimental results on three public datasets confirm the superiority of FARKA.

## Abstract

In this study, we introduce a novel Field-Agnostic Riemannian-Kernel Alignment (FARKA) method to advance the classification of motion imagination in Brain-Computer Interface (BCI) systems. BCI systems enable direct control of external devices through brain activity, bypassing peripheral nerves and muscles. Among various BCI technologies, electroencephalography (EEG) based on non-intrusive cortical potential signals stands out due to its high temporal resolution and non-invasive nature. EEG-based BCI technology encodes human brain intentions into cortical potentials, which are recorded and decoded into control commands. This technology is crucial for applications in motion rehabilitation, training optimization, and motion control. The proposed FARKA method combines Riemannian Alignment for sample alignment, Riemannian Tangent Space for spatial representation extraction, and Knowledge Kernel Adaptation to learn field-agnostic kernel matrices. Our approach addresses the limitations of current methods by enhancing classification performance and efficiency in inter-individual MI tasks. Experimental results on three public EEG datasets demonstrate the superior performance of FARKA compared to existing methods.