Motor Imagery Feature Extraction Cum Optimization for Detection of ALS Disease

Abstract

The accuracy and precision of ALS disease detection depend on EEG classification. The high dimension of EEG data and complex structure of features degraded the performance of ALS disease detection.  The feature extraction cum optimization process is better ways to achieve high accuracy and precision. The motor imagery is frequency-time series data in the mode of the signal. The brain-computer interface (BCI) uses electroencephalography or other modes for measuring brain activity. The recorded and collected signals analysed and find the abnormal condition of acute diseases such as ALS, brain stroke, and many more. Various frequency-based transform functions are used for the extraction of features, such as FFT, DCT, DWT, and many more transform functions. The CSP (common spatial pattern) is the most dominated feature extraction method for EEG feature extraction. The diverse nature of features needs features optimization process for a better process of classification. Swarm intelligence and evolutionary algorithms play an essential role in the process of feature optimization. In this paper study of various methods of feature extraction motor imagery. For the analysis of the algorithm used BC-III and BC-IV competition dataset. The experimental work carried on MATLAB Tools and measured some standard parameters for the evaluation of results