Awrangjeb, MohammadRodrigues, Jardel das C.Stantic, BelaEstivill-Castro, V. (Vladimir)2021-05-262021-05-262020Awrangjeb M, Rodrigues JDC, Stantic B, Estivill-Castro V. A fair comparison of the EEG signal classification methods for alcoholic subject identification. In: 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020); 2020 Nov 25-27; Wellington, New Zealand. New Jersey: IEEE; 2020. [6 p.]. DOI: 10.1109/IVCNZ51579.2020.92906832151-2205http://hdl.handle.net/10230/47661Comunicació presentada al 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020), celebrat del 25 al 27 de novembre de 2020 a Wellington, Nova Zelanda.The electroencephalogram (EEG) signal, which records the electrical activity in the brain, is useful for assessing the mental state of the alcoholic subject. Since the public release of an EEG dataset by the University of California, Irvine, there have been many attempts to classify the EEG signals of alcoholic' and `healthy' subjects. These classification methods are hard to compare as they use different subsets of the dataset and many of their algorithmic settings are unknown. The comparison of their published results using the inconsistent and unknown information is unfair. This paper attempts a fair comparison by presenting a level playing field where a public subset of the dataset is employed with known algorithmic settings. Two recently proposed high performing EEG signal classification methods are implemented with different classifiers and cross-validation techniques. While compared it is observed that the wavelet packet decomposition method with the Naïve Bayes classifier and the k-fold cross validation technique outperforms the other method.application/pdfeng© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/IVCNZ51579.2020.9290683info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/IVCNZ51579.2020.9290683TrainingPattern classificationFeature extractionElectroencephalographyWavelet packetsClassification algorithmsTestinginfo:eu-repo/semantics/openAccess