用線性鑑別分析法做冥想四個方向的分類
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2005
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Abstract
大腦人機介面(Brain-computer interface)是一種利用腦部訊號跟外界溝通的新技術，其目的是能夠幫助因神經肌肉損傷而行動受阻礙的人。對於內部刺激—想像左右手和腳動—已經有研究過了，但冥想四個方向是值得去研究。有鑑於此，本研究建立一套系統，它以冥想四個方向實驗的腦波訊號作為輸入訊號並利用快速傅立葉分析法找出腦波的特徵，然後利用線性鑑別分析法來分辨這些特徵。最後找出一組可以分辨冥想四個方向的腦波。
經由實驗結果得知，此系統可以利用實驗中分辨率最高的資料做為參考腦波，最佳的分辨率可達80%以上。未來可預計將此運用於人機介面上以造福神經疾病患者或行動不便人士。
The brain-computer interface (brain-computer interface) is a kind of new technology of utilizing brain signals to communicate with the external world. Its purpose is to help the person who is hindered take action because his neural muscle is damaged. Internal stimuli, such as imaging right-hand, left-hand, and foot moving, have been studied, but imaging four directions in meditation is worth being studied. For this reason, a system is developed, and the EEG of the four directions in meditation is taken as input signals and a fast Fourier analysis is used to find the features of the EEG. Then a linear discriminant analysis is adopted to classify the features. Finally, a set of brain waves, which can distinguish four directions in meditation, is obtained. From the experimental results, it is better to use the data which the accuracy of the classification is the highest in the experiment to be the reference material. The best classification rate in the experiment data is more than 80%. In the future study, the system can be applied to brain-computer interface to benefit neural disease persons or disabled persons.
The brain-computer interface (brain-computer interface) is a kind of new technology of utilizing brain signals to communicate with the external world. Its purpose is to help the person who is hindered take action because his neural muscle is damaged. Internal stimuli, such as imaging right-hand, left-hand, and foot moving, have been studied, but imaging four directions in meditation is worth being studied. For this reason, a system is developed, and the EEG of the four directions in meditation is taken as input signals and a fast Fourier analysis is used to find the features of the EEG. Then a linear discriminant analysis is adopted to classify the features. Finally, a set of brain waves, which can distinguish four directions in meditation, is obtained. From the experimental results, it is better to use the data which the accuracy of the classification is the highest in the experiment to be the reference material. The best classification rate in the experiment data is more than 80%. In the future study, the system can be applied to brain-computer interface to benefit neural disease persons or disabled persons.
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Keywords
大腦人機介面, 腦電波, 線性鑑別分析法, BCI, EEG, LDA