VMD-WSST: A Combined BCI Algorithm to Predict Self-paced Gait Intention

Prediction of movement intention is of paramount importance to enable volitional control of assistive devices. The performances of current Brain-Computer Interfaces (BCI) are yet to reach the desired degree of accuracy necessary for real-life assistive technologies. Therefore, the prediction of gait intention remains a critical topic of research. This paper proposes an algorithm by leveraging two powerful empirical time-frequency analysis tools: Variational Mode Decomposition (VMD) and Wavelet Synchrosqueezed Transform (WSST). A combination of VMD and WSST was implemented to extract high-quality features in the time-frequency plane from Electroencephalography (EEG) data collected from six healthy individuals. The data were collected while they performed self-paced repetitions of gait initiations and terminations without any external audio or visual cue. The extracted features were later used to train a Support Vector Machine (SVM) classifier with a radial basis kernel to predict the intention to start or stop the gait cycle from gait-related EEG data. The combined VMD-WSST approach reached 83.36 +/- 1.75% accuracy, 82.83 +/- 2.99% sensitivity, and 83.45 +/- 3.59% specificity in starting intention detection. While, in the case of stopping intention prediction, the classification accuracy, sensitivity, and specificity were 81.57 +/- 1.70%, 81.14 +/- 3.06%, and 82.06 +/- 3.28%, respectively. The performances obtained by the proposed methodology were better than or competitive with those obtained by numerous state-of-the-art BCI methodologies. The results of this study show promise in predicting intention to start or stop walking from EEG, which could potentially enable assistive devices to be controlled volitionally.