本文選題：腦機接口(BCI) + 腦波信號　； 參考：《計算機工程與應用》2017年11期

【摘要】：腦波信號識別是腦機接口(BCI)技術需要解決的關鍵問題之一。針對腦波信號的數(shù)據(jù)處理以及特征提取,提出了采用小波變換系數(shù)計算"專心度"的特征來描述精神力集中強烈程度以及腦電波活躍狀況,并結合實際測試得出不同精神狀態(tài)下的檢測算法。設計了一款基于ARM處理器的腦波控制軌道小車系統(tǒng),將"專心度"值發(fā)送給小車系統(tǒng),實現(xiàn)了不同精神狀態(tài)下對小車車速的控制。實驗結果表明,采用專心度的特征在識別效果方面要優(yōu)于FFT的單一頻率特征。并且該系統(tǒng)能夠快速提取出腦波特征并實時地控制軌道小車行駛,系統(tǒng)性能穩(wěn)定具有一定的實用價值。
[Abstract]:Brain wave signal recognition is one of the key problems to be solved in the brain machine interface (BCI) technology. In view of the data processing and feature extraction of brain wave signals, the wavelet transform coefficients are used to calculate the "concentration" characteristics to describe the intensity of the concentration of mental forces and the state of the EEG jump, and the different mental states are obtained in combination with the actual test. A brain wave control track car system based on ARM processor is designed, which sends the "concentration" value to the car system and realizes the control of the speed of the car in different mental states. The experimental results show that the characteristic of the concentration is better than the single frequency characteristic of FFT in the recognition effect. And the system can be used. It is of practical value to extract the characteristics of brain waves quickly and control the track car in real time.

【作者單位】： 西安科技大學電氣與控制工程學院;
【基金】：陜西省教育廳基金(No.15JK1472) 陜西省工業(yè)科技攻關基金項目(No.2015GY020)
【分類號】：TN911.7;TP23

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