Prediction of Lifted Weight Category Using EEG Equipped Headgear

Abstract

In brain-computer interface and neuroscience, electroencephalography (EEG) signals have been well studied with not only cognitive activities but also physical activities. This work investigates if EEG can be used for detecting the motion as well as the variable weights a person is lifting. To this end, we used both commercial EEG headsets as well as open-source and open-protocol EEG hardware that is suitable for do-it-yourself designers. EEG data were obtained during performing biceps flexion-extension motions for different weight categories: lifting with no weight (empty), medium, and heavy lifting. Through two experiments of the bicep curl lifting scenario, we validated the concept with a study designed according to neuroscience standards and explored the pathway towards real-world applications with wearable sensing and smart garments. Both feature-based classification methods and deep learning models were designed and evaluated, showing accuracy up to 78% of differentiating three levels of weight (empty, medium, and heavy) consistently outperforming similar the state of the art. Our approach to predict different categories of lifted weight could be used in further optimizations in different research areas such as rehabilitation, sport as well as industrial applications. To encourage further research in this direction, the data sets acquired during this study will be publicly available.