Discrimination of Walking and Standing from Entropy of EEG Signals and Common Spatial Patterns

Recently, the complexity analysis of brain activity has shown the possibility to provide additional information to discriminate between rest and motion in real-time. In this work, we propose a novel entropy-based machine learning method to classify between standing and walking conditions from the sole brain activity. The Shannon entropy has been used as a complexity measure of electroencephalography (EEG) signals and subject-specific features for classification have been selected by Common Spatial Patterns (CSP) filter. Exploiting these features with a linear classifier, we achieved > 85% of classification accuracy over a long period (≈ 25 min) of standing and treadmill walking on 11 healthy subjects. Moreover, we implemented the proposed approach to successfully discriminate in real-time between standing and over-ground walking on one healthy subject. We suggest that the reliable discrimination of rest against walking conditions achieved by the proposed method may be exploited to have more stable control of devices to restore locomotion, avoiding unpredictable and dangerous behaviors due to the delivery of undesired control commands.