Abstract
In this research, 14 stroke patient's brainwave activity with open eyes (OE) and close eyes (CE) sessions are used. This work aims to study and classify 2 activities that validate our data acquisition. The data set of each subject is used to classify the state of the subject during electroencephalogram (EEG) recording. For the classification model, the input signals are alpha, beta, theta, and delta bands. The classification algorithm used in this work is the Artificial Neural Network (ANN). The accuracy value will be obtained from each subject. There are substancial differences between the EEG signals of each patient and hence affecting the accuracy value of the subject. The results obtained from our experiment proved that ANN can be used to classify the state of the subject during data recording.
Highlights
Stroke has been one of the primary causes of worldwide disabilities
A Brain-Computer Interface (BCI) is a synthetics intelligent system able to understand a specific collection of trends in brain signals namely, acquisition of signal, pre-processing, extraction of feature, classification, and interface for the practical application of the system [14]
The Power Spectrum Density (PSD) of the EEG signal was extracted after translating it to the timefrequency domain using FFT
Summary
Stroke has been one of the primary causes of worldwide disabilities. Stroke-related motor function deficits lead to poor overall life quality [1]. Brain-Computer Interface (BCI) provides a communications medium and interaction with a system directly from the brain in an external environment, without any motor pathway being involved. The basis for regulating BCI systems is neurological phenomena which are special features of brain activity occurring in the brain signals. Various approaches to capturing brain signals and analyzing neurological phenomena were employed. One of the methods is EEG which records electrical activity along the scalp surface [2]. An EEG-based BCI detects a patient's neuronal signal as an input, enabling users to monitor their brain function effectively
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