Mar 8, 2022 · An efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the ...

The results show that our methods can improve the decoding accuracy by 2% with only. 65% computational cost significantly compared with the baseline method.

Sep 20, 2024 · The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years.

Nov 4, 2023 · We present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification ...

The G-EEGCS identifies key channels based on specific thresholds by transforming EEG signals into a correlation matrix and mapping them to an adjacency matrix.

In recent years, there has been a significant increase in research focused on decoding EEG signals for the purpose of direct brain-to-text communication [1] .

Sep 12, 2024 · In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal ...

Apr 23, 2024 · We propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals.

Sep 10, 2024 · Efficient Predefined Time Adaptive Neural Network for Motor Execution EEG Signal Classification based Brain-Computer Interaction ; Jose N N.

Oct 9, 2024 · This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest ...