In this study, Golden Sine Algorithm (Gold-SA) is presented as a new metaheuristic method for solving optimization problems. Gold-SA has been developed as a new search algorithm based ...

Electroencephalogram (EEG) signals provide important information for identification of epileptogenic area. Identification of focal EEG signals locates the epileptogenic area which is an important task for successful surgery. In this paper, convolutional neural networks (CNNs) based framework is proposed for automatic identification of focal EEG signals. The proposed intelligent system initially uses a window for randomly segment the input EEG signals. The short time Fourier transform (STFT) is applied on to the segmented EEG signals for conversion of input EEG signals into Time-Frequency (T-F) representation. The T-F representation of EEG signals are used as T-F images. Instead of training an end-to-end CNNs which necessitates more input images and time, we opt to use a pre-trained CNNs model for transfer learning. Specifically, the deep feature extraction is employed for acquiring the more convenient features from the input EEG images. The deep features extracted from AlexNet, VGG16, VGG19, and Resnet50 models used as input to different variant of k-nearest neighbor (k-NN) classifier. The conducted experimental works show that AlexNet, VGG16, and Resnet50 achieve promising results. Specifically, the fc6 layers of AlexNet, VGG16 and fc1000 layer of Resnet50 produce 99.8 $\%$ accuracy score with weighted-k-NN approache. The comparative study shows that proposed method provides better performance in comparison to state-of-the-art methods.