Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI syste...

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Veröffentlicht in:	Computers in biology and medicine 2022-04, Vol.143, p.105242-105242, Article 105242
Hauptverfasser:	Sadiq, Muhammad Tariq, Aziz, Muhammad Zulkifal, Almogren, Ahmad, Yousaf, Adnan, Siuly, Siuly, Rehman, Ateeq Ur
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Automation Brain Brain research Brain-computer interface Classification Computer applications Continuous wavelet transform Convolution neural network Datasets Deep learning EEG Electroencephalography Feature extraction Human-computer interface Imagery Implants Learning Learning rates Machine learning Magnetic resonance imaging Mental task performance Neural networks Noise reduction Observational learning Optimizers Principal components analysis Robustness Signal classification Signal processing Support vector machines Transfer learning Wavelet transforms
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