TD-LSTM: a time distributed and deep-learning-based architecture for classification of motor imagery and execution in EEG signals

Abstract

One of the critical challenges in brain-computer interfaces is the classification of brain activities through the analysis of EEG signals. This paper seeks to improve the efficacy of deep learning-based rehabilitation systems, aiming to deliver superior services for individuals with physical disabilities. The research introduces the time distributed long short-term memory (TD-LSTM) framework, which incorporates an LSTM and a time distributed approach to classify brain activities. Learning in TD-LSTM is achieved by uncovering time-dependent semantic dependencies within EEG signals over time. By extracting all discriminative and relevant spatiotemporal dependencies via TD-LSTM, valuable information on different time steps in each sequence has been obtained. Time distributed approach shortens the input time series, making learning from long time series sequences easier, and the learning process of complex temporal and spatial dependencies in time series multi-channel EEG signals becomes more efficient. The main contributions in this paper can be outlined as follows: (1) implementation of brain activity binary classification of motor imagery/execution tasks using time distributed approach via RNN network for the first time, (2) evaluation of the performance and generalizability of the proposed method on four benchmark datasets, (3) dual-purpose classification which providing an efficient ways for classifying both types of motor imagery/execution brain activity. The experimental results show that the proposed method performs well compared to several baseline research works.