This paper presents a novel therapy to recover patients from drug craving diseases, with the use of brain–computer interfaces (BCIs). The clinical protocol consists of trying to mentally repel drug-related images, and a Stroop test is used to evaluate the blue therapy effect. The method requires a BCI hardware package and a software program which communicates with the device. In order to improve the BCI detection rates, data were collected from five different healthy subjects during the training. These measurements are then used to design a better classification algorithm with respect to the default BCI classifier. The investigated algorithms are logistic regression, support vector machines, decision trees, k-nearest neighbors and Naive Bayes. Although the low number of participants is not enough to guarantee statistically significant results, the designed algorithms perform better than the default one, in terms of accuracy, F1-score and area under the curve (AUC). The Naive Bayes method has been chosen as the best classifier between the tested ones, giving a +12.21% performance boost as concerns the F1-score metric. The presented methodology can be extended to other types of craving problems, such as food, pornography and alcohol. Results relative to the effectiveness of the proposed approach are reported on a set of patients with drug craving problems.

Classification algorithms analysis for brain–computer interface in drug craving therapy

Mazzoleni M.;Bonfiglio N. S.
2019

Abstract

This paper presents a novel therapy to recover patients from drug craving diseases, with the use of brain–computer interfaces (BCIs). The clinical protocol consists of trying to mentally repel drug-related images, and a Stroop test is used to evaluate the blue therapy effect. The method requires a BCI hardware package and a software program which communicates with the device. In order to improve the BCI detection rates, data were collected from five different healthy subjects during the training. These measurements are then used to design a better classification algorithm with respect to the default BCI classifier. The investigated algorithms are logistic regression, support vector machines, decision trees, k-nearest neighbors and Naive Bayes. Although the low number of participants is not enough to guarantee statistically significant results, the designed algorithms perform better than the default one, in terms of accuracy, F1-score and area under the curve (AUC). The Naive Bayes method has been chosen as the best classifier between the tested ones, giving a +12.21% performance boost as concerns the F1-score metric. The presented methodology can be extended to other types of craving problems, such as food, pornography and alcohol. Results relative to the effectiveness of the proposed approach are reported on a set of patients with drug craving problems.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1453241
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