Key result
Combined EMD and BiLSTM framework decodes imagined ring-finger movements from MI-EEG signals with ~99% accuracy.
Why the study?
MI-EEG signals from imagined movements of small limbs present classification challenges due to reduced signal level and increased noise, limiting effective BCI use.
A framework combining Empirical Mode Decomposition and a Bidirectional LSTM architecture achieved 98.8% accuracy in decoding individual finger motor imagery EEG signals, demonstrating potential for Brain-Computer Interface applications.
Advances precise finger MI decoding for BCI; leaves open clinical validation and generalizability.
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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Mwata-Velu et al. (2022) studied Healthy (n=8). Empirical Mode Decomposition and Bidirectional LSTM vs. Raw data without EMD was evaluated on Classification accuracy of ring-finger imagined movements. A framework combining Empirical Mode Decomposition and a Bidirectional LSTM achieved an average accuracy of 98.8% in decoding ring-finger imagined movements from MI-EEG signals.
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