Improving the Quality of Life of Patients with Amyotrophic Lateral Sclerosis through an Artificial Intelligence-based Brain Potential Recognition System
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Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes a gradual loss of motor functions and ultimately severely limits both verbal and nonverbal communication. In the later stages of the disease, patients often retain consciousness and cognitive functions; however, they practically lose the ability to interact with the outside world. This situation creates the need for technological systems that can restore or compensate for communication based on brain activity.
The experimental study was conducted in the biomedical engineering laboratory “CleaveLab” of Georgian Technical University using the BioRadio wireless EEG system. The data were obtained through noninvasive electroencephalographic recording with electrodes placed according to the international 10–20 system. The recorded signals were processed in Excel and MATLAB, including preprocessing, normalization, feature extraction, and comparative evaluation of classification models.
Several machine learning models were tested in the course of the study, including Decision Tree, Support Vector Machine, and Naive Bayes. According to the obtained results, the Naive Bayes classifier demonstrated the best accuracy, achieving 72.8%. The results indicate that it is possible to recognize “yes/no” type cognitive responses based on EEG signals, and that this approach is promising for the development of communication systems for patients with ALS.
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