Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset

TL;DR

This paper presents a robust EEG-based BCI system that decodes multi-class motor intentions with high accuracy by optimizing data selection, demonstrating potential for controlling robotic devices.

Contribution

The study introduces a time interval selection technique that enhances EEG decoding accuracy for motor intentions, improving BCI performance over previous methods.

Findings

70.73% accuracy for motor execution tasks

47.95% accuracy for motor imagery tasks

Effective decoding of five grasping classes

Abstract

A brain-computer interface (BCI) based on electroencephalography (EEG) can be useful for rehabilitation and the control of external devices. Five grasping tasks were decoded for motor execution (ME) and motor imagery (MI). During this experiment, eight healthy subjects were asked to imagine and grasp five objects. Analysis of EEG signals was performed after detecting muscle signals on electromyograms (EMG) with a time interval selection technique on data taken from these ME and MI experiments. By refining only data corresponding to the exact time when the users performed the motor intention, the proposed method can train the decoding model using only the EEG data generated by various motor intentions with strong correlation with a specific class. There was an accuracy of 70.73% for ME and 47.95% for MI for the five offline tasks. This method may be applied to future applications, such as controlling robot hands with BCIs.