Realization of EEG-based multi-label classification with Convolutional Neural Networks
DOI:
https://doi.org/10.22399/ijcesen.3606Keywords:
Convolutional Neural Networks, EEG signal , Ricatti equation , Keras – Tensor FlowAbstract
This paper presents a method for multi-label classification on source EEG signal datasets using DCNN and GPReLU with various signal patches. Performance has been appraised using ROC Curves for multi-label classification. Emotion is a phenomenal neurological expression that releases bio-signals with electrical voltage in the brain, which is read through as EEG signals. Reading the EEG signals and analyzing them is carried out for many purposes in science and technology. A common DCNN can simply classify the signal datasets, which often is inaccurate, although classified signal entities may contain features of other classes. Classification in uncertain data leads to class imbalances. Therefore, multi-label classification suffices the need for data analysis which anatomizes the EEG signal data as containing features of many categories. A DCNN with Geometric Parameterized ReLU is introduced to smoothen the pooling activity and work swiftly on various image patches of the source EEG signal image datasets.
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