EEG signal analysis and classification on P300 speller-based BCI performance in ALS patients

Abstract

Objective: the objective of the presented work is to analyse the electroencephalography signal based on brain computer interface by using P300 speller for amyotrophic lateral sclerosis (ALS) patients and perform classification on extracted features to get accuracy. Analysis/methods: amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that involves the degeneration and death of nerve cell in the brain. It affects the process related to speech and loss of motor function in the patient. BCI technology is a communication solution for all amyotrophic lateral sclerosis (ALS) patients. The P300 speller included in the BNCI Horizon 2020 data is an application allows calculating the accuracy of classifier, which is necessary for the user to spell letters or sentences in a BCI speller paradigm. In this paper, we have extracted wavelet and power spectral density features. Association rule mining and ranking method is used for feature selection. For the classification, we have used multiple techniques and different classifiers and out of those, ten best techniques are selected based on their good performance. Finding: as a result, we get maximum 75% accuracy when we used random committee classifier.