EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface

TL;DR

This study demonstrates that EEG signals can effectively predict turn intentions (left, right, straight) for lower limb movement before the actual movement occurs, enabling improved brain-computer interface control.

Contribution

It introduces a novel EEG-based method for pre-movement turn intention detection with high accuracy using feature extraction, selection, and machine learning classifiers.

Findings

Achieved over 81% accuracy in turn intention classification.

Identified optimal EEG window of 1.5 seconds with 0 second lag.

Validated feasibility of pre-movement intention prediction for BCI applications.

Abstract

Electroencephalography (EEG)--based turn intention prediction for lower limb movement is important to build an efficient brain-computer interface (BCI) system. This study investigates the feasibility of intention detection of left-turn, right-turn, and straight walk by utilizing EEG signals obtained before the event occurrence. Synchronous data was collected using 31-channel EEG and IMU-based motion capture systems for nine healthy participants while performing left-turn, right-turn, and straight walk movements. EEG data was preprocessed with steps including Artifact Subspace Reconstruction (ASR), re-referencing, and Independent Component Analysis (ICA) to remove data noise. Feature extraction from the preprocessed EEG data involved computing various statistical measures (mean, median, standard deviation, skew, and kurtosis), and Hjorth parameters (activity, mobility, and complexity). Further, the feature selection was performed using the Random forest algorithm for the dimensionality reduction. The feature set obtained was utilized for 3-class classification using XG boost, gradient boosting, and support vector machine (SVM) with RBF kernel classifiers in a five-fold cross-validation scheme. Using the proposed intention detection methodology, the SVM classifier using an EEG window of 1.5 s and 0 s time-lag has the best decoding performance with mean accuracy, precision, and recall of 81.23%, 85.35%, and 83.92%, respectively, across the nine participants. The decoding analysis shows the feasibility of turn intention prediction for lower limb movement using the EEG signal before the event onset.