Finger tapping test is a common testing item for patients with Parkinson's disease (PD) in clinical neurology. It mainly evaluates the fine motor function of patient's hand in three aspects:amplitude, speed and regularity of the movement. This paper focused on the quantitative assessment of regularity of finger tapping movement for PD patients. The movement signals of thumb and index finger were recorded by using inertial sensor unit in the process of tapping test. Two nonlinear dynamic indexes, approximate entropy (ApEn) and sample entropy (SampEn), were calculated, and then the values were statistically analyzed. The experimental results indicated that both indexes had significant differences between patient group and control group. Moreover, the indexes had relatively high correlation with the scores of corresponding unified Parkinson's disease rating scale (UPDRS) item rated by clinical clinician, which illustrated that these two indexes could reflect the injury level of the repetitive finger movement. So, as a reliable method, it can be provided to the clinical evaluation of hand movement function for PD patients.
The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.