| dc.description.abstract | Aphasia is a disease that can seriously affect the language abilities of patients. The primary cause is usually stroke, but it can also be caused by other factors such as brain trauma, tumors, and degeneration. The most common clinical manifestations in patients include difficulty finding words, leading to speech errors, substituting target words with other words, and difficulties with auditory comprehension, reading, and writing.Common diagnostic methods for aphasia include clinical scales and non-invasive brain stimulation, such as repetitive transcranial magnetic stimulation (rTMS), combined with intensive language training. The former method is more subjective and requires professional evaluation from a speech therapist, while the latter is more expensive. Therefore, an effective and objective diagnostic method is crucial for language therapy.This study proposes an automated method for evaluating aphasia. In this experiment, machine learning (ML) is used to design an algorithm model for automatic assessment, combining self-developed VR language training modules to obtain behavioral and physiological information from task execution. The performance and situations of patients in various language task training are analyzed through machine learning. The research results will be discussed from two aspects: statistical analysis and machine learning.In the statistical analysis, we will conduct a Mann-Whitney U test on the multimodal physiological and game task features between normal individuals and aphasia patients, showing many significant differences compared to normal individuals (p < 0.05). In the machine learning aspect, the evaluation results show good performance, with all models achieving over 80% accuracy. | en_US |