Abstract
Motor Imagery Electroencephalogram (MI-EEG) signals are widely used in Brain-Computer Interfaces (BCI). MI-EEG signals of large limbs movements have been explored in recent researches because they deliver relevant classification rates for BCI systems. However, smaller and noisy signals corresponding to hand-finger imagined movements are less frequently used because they are difficult to classify. This study proposes a method for decoding finger imagined movements of the right hand. For this purpose, MI-EEG signals from C3, Cz, P3, and Pz sensors were carefully selected to be processed in the proposed framework. Therefore, a method based on Empirical Mode Decomposition (EMD) is used to tackle the problem of noisy signals. At the same time, the sequence classification is performed by a stacked Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed method was evaluated using k-fold cross-validation on a public dataset, obtaining an accuracy of 82.26%.
Highlights
The EEG Brain-Computer Interface (BCI) dataset was built by Kaya et al [18], considering five interaction paradigms related to motor imagery
The data initially collected was intended to be used in BCI systems; the accurate decoding of the signals in real applications is still a challenge
The Empirical Mode Decomposition (EMD) method was used as a preprocessing step of Motor Imagery Electroencephalogram (MI-EEG) signals from the C3, Cz, P3, and Pz sensors
Summary
Brain-Computer Interface (BCI) spellers using code-modulated Visual Evoked Potentials (cVEP) help patients with
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