Fusion of Spatial and Riemannian Features to Enhance Detection of Gait Adaptation Mental States During Rhythmic Auditory Stimulation

Music has a powerful effect in entraining brain networks that influence both affective states and motor control. The use of Rhythmic Auditory Stimulation (RAS) has shown promising results in regularising and stabilising gait control in patients with neurological problems while alleviating associated depressive symptoms. Brain-computer interfaces (BCIs) can play a pivotal role in shaping music stimulus during these interventions. However, this requires robust detection of mental states during gait adaptation. In this work we investigate the use of Regularised Common Spatial Patterns (RCSP) and Riemannian geometry to detect gait states based on Electroencephalogram (EEG) signals. RCSP are particularly effective on small and noisy datasets while reducing overfitting. Riemannian geometry has proven powerful in analysing the covariance structure of EEG signals that reflect functional brain connectivity. Using a publicly available dataset, we extensively evaluate our methods using two dataset splits. We demonstrated statistically significant results in the dataset split 'individual subjects' with the combination of Regularised Common Spatial Patterns (RCSP) and Riemannian geometry.