Brain-computer interface (BCI) is an emerging technology
which provides a road to control communication and external
devices. Electroencephalogram (EEG)-based motor imagery (MI) tasks
recognition has important research significance for stroke,
disability and others in BCI fields. However, enhancing the
classification performance for decoding MI-related EEG signals
presents a significant challenge, primarily due to the variability
across different subjects and the presence of irrelevant
channels. To address this issue, a novel hybrid structure is
developed in this study to classify the MI tasks via deep separable
convolution network (DSCNN) and bidirectional long short-term memory
(BLSTM). First, the collected time-series EEG signals are initially
processed into a matrix grid. Subsequently, data segments formed
using a sliding window strategy are inputted into proposed DSCNN
model for feature extraction (FE) across various dimensions. And,
the spatial-temporal features extracted are then fed into the BLSTM
network, which further refines vital time-series features to
identify five distinct types of MI-related tasks. Ultimately, the
evaluation results of our method demonstrate that the developed
model achieves a 98.09% accuracy rate on the EEGMMIDB physiological
datasets over a 4-second period for MI tasks by adopting full
channels, outperforming other existing studies. Besides, the results
of the five evaluation indexes of Recall, Precision, Test-auc, and
F1-score also achieve 97.76%, 97.98%, 98.63% and 97.86%,
respectively. Moreover, a Gradient-class Activation Mapping
(GRAD-CAM) visualization technique is adopted to select the vital
EEG channels and reduce the irrelevant information. As a result, we
also obtained a satisfying outcome of 94.52% accuracy with 36
channels selected using the Grad-CAM approach. Our study not only
provides an optimal trade-off between recognition rate and number of
channels with half the number of channels reduced, but also it can
also advances practical application research in the field of BCI
rehabilitation medicine, effectively.