Alcohol use disorders automatic detection based BCI systems: a novel EEG classification based on machine learning and optimization algorithms
AbstractAlcohol is a serious toxic substance that alters brain function by interfering with neuron processes in the central nervous system, leading to mental and behavioral disorders. Alcoholism has serious pathological effects on the liver, immune system, brain, and heart. The diagnosis of alcoholism is important not only because of its impact on individuals and society but also because of the cost to the national health system, as many people around the world suffer from the disease. These diseases can be diagnosed by automatically classifying normal and alcohol electroencephalogram (EEG) signals. Such that this work contains a simple and very fast prediction system. The method uses a bandpass filter to remove all unused signal frequencies, it can be showed in correlation matrices. Then the use of the machine learning algorithms in the classification stage that characterized by its high classification and prediction speed over than 400 and 25500 samples per second respectively, also, the use of optimization algorithms (GA and HHO) can be increased all accuracy values to more than 99.5% when using all electrodes and without importing data decomposition algorithms. To minimize the number of the electrodes and remained good accuracy values, this work uses the Extra-Trees algorithm, such as with only four electrodes the accuracy value remains higher of 99\%. A comparison with other techniques was performed aiming to validate our approach, and it shows great efficiency, simplicity, and instantly.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
In order for iJIST to publish and disseminate research articles, we need publishing rights. This is determined by a publishing agreement between the author and iJIST.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.