Time Series Shapelet-Based Movement Intention Detection Toward Asynchronous BCI for Stroke Rehabilitation

Abstract

Brain computer interface (BCI) systems for neurorehabilitation have received increasing attention over the past decade. These systems provide an alternative approach to restore lost motor functions in stroke patients by inducing brain plasticity. To utilize BCI systems for stroke rehabilitation, user movement intentions from electroencephalogram are used as triggers for rehabilitation tools such as electrical stimulators. A reliable movement intention detection plays a vital role in an effective rehabilitation to establish a relation between movement intentions and corresponding feedback from a rehabilitation tool. We therefore propose a novel movement intention detection algorithm based on time series shapelets. We collected a dataset of subjects performing a self-pace ankle dorsiflexion and tested the algorithm in both classification and pseudo-online detection. The classification results demonstrate that our proposed algorithm significantly outperforms six other algorithms and achieves the highest classification performance with F1-score of 0.82. For pseudo-online detection, our algorithm gains very high performance in all performance metrics with 69% True Positive Rate (TPR), 8 False Positives per minute (FPs/min), and 44 ms Detection Latency (DL). Our proposed algorithm does not only provide competitive performance to state-of-the-art algorithms in terms of TPR, but also maintain low FPs/min and DL. In addition, the DL of our proposed algorithm is low enough to induce effective brain plasticity for neurorehabilitation. These promising results enlighten the development of asynchronous BCI systems based on time series mining techniques to enhance stroke rehabilitation results.