Applying Auto-Regressive Model’s Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients’ Data

(E-pub Ahead of Print)

Author(s): Mridu Sahu*, Saumya Vishwal , S. Usha Srivalli, Naresh K. Nagwani, Shrish Verma, Sneha Shukla .

Journal Name: Current Medical Imaging

Become EABM
Become Reviewer

Abstract:

Objective: 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). Method/Analysis: 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. Findings: 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).

Keywords: Amyotrophic Lateral Sclerosis, Auto Regressive Model, Brain Computer Interface, Electroencephalography, Model Fitting, P300 Speller, Time Series Model, Yule Walker

Rights & PermissionsPrintExport Cite as

Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1573405614666180322143503
Price: $95