Browsing by Author "Chang, Wan-Lin"

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Assessment of Musical Training Induced Neuroplasticity by Auditory Event Related Potentials and Neural Networks
(2009)
Liang, Sheng-Fu
;
Liu, Chi-Sheng
;
Chang, Wan-Lin
;
Tsao, Yu-Hsiang
;
Ko, Li-Wei
Music provides a tool to study numerous aspects of neuroscience from motion-skill to emotion since listening to and producing music involves many brain functions. The musician\'s brain is also regarded as an ideal model to investigate plasticity of the human brain. In this paper, an EEG-based neural network is proposed to assess neuroplasticity induced by musical training. A musical chord perception experiment is designed to acquire and compare the behavioral and neural responses of musicians and non-musicians. The ERPs elicited by the consonant and dissonant chords are combined together as the features of the model. The principle component analysis (PCA) is used to reduce feature dimensions and the dimension-reduced features are input to a feedforward neural network to recognize the brain potentials belong to a musician or a non-musician. The accuracy can reach 97% in average for leave-one-out cross validation of six subjects in this experiment. It demonstrates the feasibility of assessing effects of musical training by ERP signals elicited by musical chord perception.
EEG-based Absence Seizure Detection Methods
(2010)
Liang, Sheng-Fu
;
Chang, Wan-Lin
;
Chiueh, Herming
;
電機工程學系
;
Department of Electrical and Computer Engineering
Approximately 1 % of people in the world have epilepsy and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. An automatic seizure detection system can reduce the time taken to review the EEG data by the neurologist for epilepsy diagnosis. In this paper, various EEG features integrated with the linear or non-linear classifiers are evaluated for seizure detection. For the EEG features, approximate entropy (ApEn) combined with 1) EEG power spectra or 2) autoregressive model (AR) are compared. In addition, the principle component analysis (PCA) is also utilized for feature extraction. For the classifiers, two linear models, linear least square (LLS) and linear discriminant analysis (LDA), and two nonlinear models, backpropagation neural network (BPNN) and support vector machine with radial basis function kernel (RBFSVM) are compared. The EEG signals of three Long Evans rats with spontaneous absence seizures are used for leave-one-out cross-validation. Experimental results shows that combining ApEn and multi-band EEG power spectra are superior to the combination of ApEn and AR model for all classifiers. The best average accuracy is 97.5% performed by RBFSVM and the linear models can achieve to higher than 95%. The automatic seizure detection method can be utilized to drive the seizure warning device or seizure control devices in the future to enhance the patients\' quality of life.