A Combined Projection for Remote Control of a Vehicle Based on Movement Imagination: A Single Trial Brain Computer Interface Study

Abstract

Disabled patients using brain computer interface (BCI) applications have a more convenient life. The present study implements an electroencephalogram (EEG)-based signal processing algorithm for controlling a wireless mobile vehicle through imagination. The aim is to improve the filtered common spatial pattern (CSP) algorithm for BCI applications. The proposed method is a combination of the CSP projection with a Modified Secondary Projection of the filtered Common Spatial Pattern (MSPCSP). With this algorithm, distinctive differential features are obtained from the combination of the MSPCSP and CSP projection eigenvalues to identify four classes: moving-forward-for-pause, stop-for-pause, moving-forwardcontinuously, and stopped-continuously. The second contribution is the design of a task to produce clear imaginary movement patterns. The task is a combination of brain stimulation by viewing red and yellow sketches of the right hand that indicate opening the hand and making a fist. Eighteen subjects participated in the experiment for wireless control of a mobile vehicle in offline and real-time modes. The results were then evaluated through an accuracy and paired t-test statistical analysis for offline and real-time signal processing. The results based on the MSPCSP projection showed significant improvements in accuracy in comparison with the CSP projection: 82.16±9.04% with p < 0.05 and 70.83±8.27% for offline and real-time processing, respectively. In addition, the MSPCSP projection attained higher accuracies of 14.72% and 13.33% for offline and real-time processing, respectively. It was concluded that the MSPCSP projection generates more discriminant differential features than the filtered CSP projection. Further, the MSPCSP projection with the thresholds extend the limitation of CSP-based methods from two-to four-class identification.INDEX TERMS Brain computer interface (BCI), remote vehicle control, common spatial pattern, feature extraction, threshold classifier.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery

“…Moreover, numerous research papers have investigated the integration of deep learning networks with steering interfaces, aiming to establish systems capable of translating users' brain activity into movement instructions for vehicles, such as a hexapod [8], a telepresence robot control interface based on a support vector machine (SVM) [9,10], wheelchair control based on motor imagery and fuzzy logic [11], multi-scale CNNs [12], multilevel weighted feature fusion [13], and power spectrum estimation [14]. Despite the great progress in the field of interpreting human thoughts, the control over the vehicle is often limited to a single direction [8,15]. Despite the progress in decoding human brain activities, it remains challenging to implement BCIs in real-life applications.…”

Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report

“…Brain-computer interfaces (BCIs) have emerged as a reliable assistive and rehabilitative technology with proven clinical efficacy for cognitive and motor skills enhancement in healthy subjects [1,2] and patients with motor disabilities resulting from focal lesions [3][4][5]. Preliminary evidence suggests that stroke patients' ability to perform movement-related tasks is similar to healthy subjects [6,7], in which specific brain regions are engaged resulting in a decrease or increase in electroencephalographic (EEG) activity (with respect to a baseline) known as event-related desynchronization or synchronization (ERD/ERS) [8].…”