Pehlivan, SudeAkdemir, OnurCura, O.K.Akbuğday, BurakAkan, Aydın2023-12-262023-12-26202397894645936002219-5491https://doi.org/10.23919/EUSIPCO58844.2023.10289787https://hdl.handle.net/20.500.14365/502031st European Signal Processing Conference, EUSIPCO 2023 -- 4 September 2023 through 8 September 2023 -- 194070Attention-Deficit/Hyperactivity Disorder (ADHD) is a common mental disorder affecting both children and adults. It is characterized by issues with concentration, hyperactivity, and impulsivity, which can interfere with everyday duties and interpersonal relationships. Although behavioral studies are utilized to treat the disease, there is no proven method for detecting it. The Electroencephalogram (EEG) is a non-invasive method that monitors electrical activity in the brain and is commonly used to identify neurological and mental illnesses such as ADHD. In this study, the topographic EEG feature maps (EEG-FMs) were obtained from 6 traditional time-domain characteristics known as Hjorth activity, Hjorth mobility, Hjorth complexity, kurtosis, and skewness. The feature maps were concatenated and used as input to Convolutional Neural Network (CNN) model for ADHD classification. To show the efficacy of the recommended approach, EEG data from 15 ADHD individuals and 18 control subjects (CS) were analyzed. The results showed that concatenated EEG-FMs were successful to classify ADHD with up to 99.72% accuracy. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.eninfo:eu-repo/semantics/closedAccessAttention Deficit Hyperactivity Disorder (ADHD)Convolutional Neural Network (CNN)EEGFeature MapBiomedical signal processingBrainConvolutionConvolutional neural networksDiseasesHigher order statisticsNoninvasive medical proceduresTime domain analysisAttention deficit hyperactivity disorderBehavioural studiesConvolutional neural networkFeature mapInterpersonal relationshipMental disordersNoninvasive methodsTopographic featuresElectroencephalographyConference Object10.23919/EUSIPCO58844.2023.102897872-s2.0-85178321257