Data processing, network architectures, statistics and visualization pertaining to the use of convolutional neural networks for feature extraction and classification of imagined speech EEG recordings. A nested cross-validation technique has been employed to validate certain hyper-parameters. The performance of the CNNs is validated against classification obtained using a SVM with mel frequency cepstral coefficients (MFCC) as features.
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Data processing, network architectures, statistics and visualization pertaining to the use of convolutional neural networks for feature extraction and classification of imagined speech EEG recordings.
massoudkh29/imagined_speech_cnns
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Data processing, network architectures, statistics and visualization pertaining to the use of convolutional neural networks for feature extraction and classification of imagined speech EEG recordings.
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