Empirical Mode Decomposition and a Bidirectional LSTM Architecture Used to Decode Individual Finger MI-EEG Signals


Motor Imagery (MI)
Binary Classification
Electroencephalogram (EEG)
Empirical Mode Decomposition (EMD)
Bidirectional Long Short-Term Memory (BiLSTM)

How to Cite

Mwata-Velu, T., Ruiz-Pinales, J., Avina-Cervantes, J. G., Gonzalez-Barbosa, J. J. ., & Contreras-Hernandez, J. L. . (2022). Empirical Mode Decomposition and a Bidirectional LSTM Architecture Used to Decode Individual Finger MI-EEG Signals. Journal of Advances in Applied & Computational Mathematics, 9, 32–48. https://doi.org/10.15377/2409-5761.2022.09.3


Brain-Computer Interface (BCI) paradigms based on Motor Imagery Electroencephalogram (MI-EEG) signals have been developed because the related signals can be generated voluntarily to control further applications. Researches using strong and stout limbs MI-EEG signals reported performing significant classification rates for BCI applied systems. However, MI-EEG signals produced by imagined movements of small limbs present a real classification challenge to be effectively used in BCI systems. It is due to a reduced signal level and increased noisy distorted effects. This study aims to decode individual right-hand fingers’ imagined movements for BCI applications, using MI-EEG signals from C3, Cz, P3, and Pz channels. For this purpose, the Empirical Mode Decomposition (EMD) preprocesses the non-stationary and non-linear EEG signals to finally use a Bidirectional Long Short-Term Memory (BiLSTM) to classify corresponding feature sequences. An average accuracy of 98.8 % was achieved for ring-finger movements decoding using k-fold cross-validation on a public dataset (Scientific-Data). The obtained results support that the proposed framework can be used for BCI control applications.



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Copyright (c) 2022 Tat'y Mwata-Velu, Jose Ruiz-Pinales, Juan Gabriel Avina-Cervantes, Jose Joel Gonzalez-Barbosa, Jose Luis Contreras-Hernandez