Statistical Analysis of Subject-Specific EEG data during Stroke Rehabilitation Monitoring

摘要

Monitoring stroke rehabilitation (MSR) program is a crucial part in controlling the progression of brain recovery activity during rehabilitation treatment. MSR usually is done using manual observation by clinicians. However, recent practices show high subjectivity depending on observations and evaluators, besides less sensitivity regarding the small changes that occur during the rehabilitation progress. Many stroke patients have joined the rehabilitation program with unclear result due to the difficulties to monitor the progress during rehabilitation. Recently EEG technologies have been used widely to study stroke patients. EEG is a device that can record electrical activity along the scalp, so small changes happen in the brain regarding the patient’s capability during rehabilitation then can be captured. This study implements EEG for monitoring the stroke rehabilitation process by analyzing EEG parameters that can be used to seeing the progress of rehabilitation. In this study, four-stroke patients are participating in the physical therapy rehabilitation program using the Bobath method on hand function. EEG evaluation is done on pre-test and post-test in each treatment, by placing two electrodes of C3 and C4 on patients’ scalp. In the preprocessing stage, Finite Impulse Response (FIR) is used to filter the band of EEG raw data. Cleanline algorithm is used to clean the EEG from electrophysiology and sinusoidal current. Noise artefact is then filtered using the Artifact Subspace Reconstruction (ASR) algorithm, where as ICA is used to decompose the EEG after ASR. EEG data is then classified into three frequency bands such as Alpha, Beta High, and Beta Low. The statistical features that were used are Power Spectral Density (PSD), Power Percentage (PP), Standard Deviation (STD), and Mean Absolute Value (MAV). The analysis is applied to individual data in evaluating the progress of rehabilitation between pre-test and post-test in each treatment. The results show that MAV and PSD are the dominant parameters when monitoring the progress of stroke rehabilitation.