Tensor Factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

One of the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-term memory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.