Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification ...
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Brain-computer interfaces (BCIs) hold great promise in biomedical engineering, particularly for diagnosing critical diseases. Motor imagery (MI) EEG classification, a key BCI process, faces challenges due to the complexity and non-stationary nature of EEG signals. These signals, recorded via electrodes, are digitized and analyzed using feature extraction techniques like FFT, STFT, CSP, and wavelet transforms, with wavelet transform being the most effective.This study proposes a deep neural network-based classification algorithm with teacher-learning-based optimization for feature refinement. Tested on a standard BCI dataset in MATLAB, the algorithm surpasses Bayesian and ensemble machine learning classifiers, enhancing classification accuracy and BCI system performance.
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Add this copy of Feature Optimization of Motor Imagery EEG to cart. $38.65, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2024 by LAP Lambert Academic Publishing.
Add this copy of Feature Optimization of Motor Imagery Eeg to cart. $46.61, new condition, Sold by Just one more Chapter rated 3.0 out of 5 stars, ships from Miramar, FL, UNITED STATES, published 2024 by LAP LAMBERT Academic Publishin.
