Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. ,  consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically,  consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state of the art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences beten the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in general.
['Andreas Antoniades', 'Loukianos Spyrou', 'Clive Cheong Took', 'Saeid Sanei']
I am writing a research paper on a idea Deep learning for epileptic intracranial EEG data
[{"from": "human", "value": "I am writing a research paper on a idea Deep learning for epileptic intracranial EEG data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. ,  consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically,  consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state of the art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences beten the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in general."}]