Search

Background: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI). Methods: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction. Results: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64). Conclusion: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.

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.