Abstract
This paper has made a survey on motor imagery EEG signals and different classifiers to analyze them. Resolution for medical images like CT, MRI can be improved using deep sense CNN and improved resolution technology. Drowsiness of a student can be analyzed using deep CNN and it helps in teaching, assessment of the student. The authors have proposed 1D-CNN with 2 layers and 3 layers architecture to classify EEG signal for eyes open and eyes closed conditions. Various activation functions and combinations are tried for 2-layer 1D-CNN. Similarly, various loss models are applied in compile model to check the CNN performance. Simulation is carried out using Python 2.7 and 1D-CNN with 3 layers show better performance as it increases number of training parameters by increasing number of layers in the architecture. Accuracy and kappa coefficient increase whereas hamming loss and logloss decreases by increasing number of layers in CNN architecture.
Highlights
EEG signals may be affected by artifacts at the time of recording
PROPOSED CNN ARCHITECTURES CNN stands for convolutional neural network. 1D-CNN can be applied on EEG signals and we proposed two architectures of 1D-CNN shown below
Motor imagery EEG is collected from open source in which eyes closed and eyes open conditions are the movements/motor considered while capturing EEG
Summary
EEG signals may be affected by artifacts at the time of recording. Adaptive classifiers with weighted distance nearest neighbor classifiers with auto regressive models, power being the features considered can give better classification performance Generalized RNN is used to detect prestate seizures in EEG. Ten sub frequency bands are created from EEG, features are extracted using regression neural network and applied to ten threshold mechanisms for classification 3 illustrates the proposed architectures for 1D-CNN, Section 4 show the results obtained using python 2.7 and narrates the possibility to increase the performance of CNN, Section 5 concludes the work carried out
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