Online LDA based brain-computer interface system to aid disabled people

TL;DR

This paper presents an online brain-computer interface system utilizing P300 EEG signals to assist disabled individuals in daily communication, achieving high accuracy in real-time object selection.

Contribution

The study introduces a real-time BCI system based on P300 detection with a linear discriminant analysis classifier, demonstrating effective assistance for disabled users.

Findings

Achieved 90.83% accuracy in online P300 detection

System classifies user intent within 15 seconds

Validated with 19 volunteers in real-time experiments

Abstract

This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance