LSTM-Based EEG Classification in Motor Imagery Tasks

By Ping Wang, A. Jiang, Xiaofeng Liu, Jing Shang, Li Zhang. IEEE transactions on neural systems and rehabilitation engineering, 2018

322 citations recorded by the source corpus.

Classification of motor imagery electroencephalograph signals is a fundamental problem in brain–computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b099ea499dbd4e25cf7a0ff996ec69db8665f739

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

In Signal Processing

Citations in the corpus

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “The majority of RNN-based studies used two RNN layers, and one study explicitly investigated the effect of the number of RNN layers [80].”
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020, background): “important information about the ambiguity of brain activities [38]–[42].”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “In [82], the authors also compared performances while varying the number of LSTM layers and found that there was significant improvement when using two layers versus one layer, whereas accuracy essentially remained even with the introduction of a third LSTM layer.”