Kaan BakırcıogluMusa Bindawa TanimuNalan ǑzkurtMustafa SeçmenCüneyt GüzelişOsman YıldızTanimu, Musa BindawaBakırcıoglu, KaanGüzeliş, CüneytYıldız, OsmanSeçmen, MustafaÖzkurt, NalanM.N. Seyman2025-10-0620229783031969430, 9783031944413, 9783032014719, 9783642039775, 9783031717154, 9783319737119, 9783030955304, 9783642236341, 9783031606649, 9783030164461978303101983818678211, 1867822X1867-821110.1007/978-3-031-01984-5_102-s2.0-85130288473https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130288473&doi=10.1007%2F978-3-031-01984-5_10&partnerID=40&md5=faadb30c00c5c85cf91ad2a034b2440ehttps://gcris.yasar.edu.tr/handle/123456789/8825https://doi.org/10.1007/978-3-031-01984-5_10In this study a convolutional neural network (CNN) is designed to identify multi-channel raw electroencephalograph (EEG) signals obtained from different subjects. The dataset contains 14 channel EEG signals taken from 21 subjects with their eyes closed at a resting state for 120 s with 12 different stimuli. The resting state EEG waves were selected due to better performance in classification. For the classification a Convolutional Neural Network (CNN) was custom designed to offer the best performance. With the sliding window approach the signals were separated into overlapping 5 s windows for training CNN better. fivefold cross-validation was used to increase the generalization ability of the network. It has been observed that while the proposed CNN is found to give a correct classification rate (CCR) of 72.71% the CCR reached the level of average 83.51% by using 4 channels. Also this reduced the training time from 626 to 306 s. Therefore the results show that usage of specific channels increases the classification accuracy and reduces the time required for training. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessBiometric Systems, Convolutional, Electroencephalograph, Multi-channel, Neural Networks, Biometrics, Convolutional Neural Networks, Electroencephalography, Biometric Systems, Classification Rates, Convolutional Neural Network, Cross Validation, Electroencephalograph Signals, Multi Channel, Neural-networks, Performance, Resting State, Sliding Window, ConvolutionBiometrics, Convolutional neural networks, Electroencephalography, Biometric systems, Classification rates, Convolutional neural network, Cross validation, Electroencephalograph signals, Multi channel, Neural-networks, Performance, Resting state, Sliding Window, ConvolutionConvolutionalElectroencephalographMulti-channelBiometric SystemsNeural NetworksConference Object