Title:Applying Auto-Regressive Model’s Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients’ Data
VOLUME: 15 ISSUE: 8
Author(s):Mridu Sahu*, Saumya Vishwal , Srungaram Usha Srivalli, Naresh Kumar Nagwani, Shrish Verma and Sneha Shukla
Affiliation:Department of Information Technology, National Institute of Technology, Raipur, Department of Information Technology, National Institute of Technology, Raipur, Department of Information Technology, National Institute of Technology, Raipur, Department of Computer Science and Technology, National Institute of Technology, Raipur, Department of Electronics and Telecommunication Engineering, National Institute of Technology, Raipur, Department of Information Technology, National Institute of Technology, Raipur
Keywords:Amyotrophic lateral sclerosis, auto-regressive model, brain computer interface, electroencephalography, model
fitting, P300 speller, time series model, Yule-Walker.
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).
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.
