Schematic representation: In the proposed stack method extracted features are used to train the proposed stacked model.

<p>In the proposed fft method extracted features are transformed using fft transformation, and then used to train the proposed stacked model for classification. Classified signals are used to generate commands to control prosthetic hand-gripping control.</p>

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Bibliographic Details
Main Author:	Jamila Akhter (21144252) (author)
Other Authors:	Hammad Nazeer (21144255) (author), Noman Naseer (6909227) (author), Rehan Naeem (11851347) (author), Karam Dad Kallu (13429833) (author), Jiye Lee (1346235) (author), Seong Young Ko (21144258) (author)
Published:	2025
Subjects:	Cell Biology Neuroscience Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified twenty participants engaged thereby enabling individuals systems recognize patterns generate control commands convolutional neural networks gripping motor activity performed using nirslab stack method classifies classification accuracies respectively 16 %), lstm motor disabilities 46 %), features using classification using stack model classification stack xlink "> term memory significantly higher results show regain autonomy proposed stack one another lstm ), infrared spectroscopy improved performance functional near fft methods features extraction feature extraction extract features effectively used deep learning computer interface classification performance class problems class hand brain signals bidirectional long based brain
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Schematic representation: In the proposed stack method extracted features are used to train the proposed stacked model.

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