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
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