Hybrid convolution (1D/2D)-based adaptive and attention-aided residual DenseNet approach on brain-computer interface for automatic imagined speech recognition framework

Neuroscience and rehabilitation may benefit from the use of a Brain-Computer Interface (BCI) based on Electroencephalograms (EEG). A person with neurological impairment benefits from this process since they express their thoughts to the outside world without the need for any devices. The process of...

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Veröffentlicht in:	Computer speech & language Jg. 96; S. 101866
Hauptverfasser:	Anusha, Jalagadugu, Chandramohan Reddy, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2026
Schlagworte:	Brain Computer Interface Electroencephalogram Empirical Mean Curve Decomposition Enhanced Heap-Based Optimizer Algorithm Hybrid Convolution (1D-2D)-based Adaptive and Attention Residual DenseNet Imagined speech recognition Imagined speech recognition Enhanced Heap-Based Optimizer Algorithm Brain Computer Interface Electroencephalogram Empirical Mean Curve Decomposition Hybrid Convolution (1D-2D)-based Adaptive and Attention Residual DenseNet
ISSN:	0885-2308
Online-Zugang:	Volltext
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