| Autor: |
Tat'y Mwata-Velu, Jose Ruiz-Pinales, Juan Gabriel Avina-Cervantes, Jose Joel Gonzalez-Barbosa, Jose Luis Contreras-Hernandez |
| Rok vydání: |
2022 |
| Zdroj: |
Journal of Advances in Applied & Computational Mathematics. 9:32-48 |
| ISSN: |
2409-5761 |
| DOI: |
10.15377/2409-5761.2022.09.3 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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