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摘要脑电波是由脑细胞所产生的生物信号。自从上个世纪20年代末德国科学家发现脑电波信号后，它就被用于神经系统疾病诊断、脑功能研究等方面。人们希望借助脑电信号解读人的思想或意图。但脑电波信号本身非常微弱，其提取必须通过“脑—机接口”(Brain Computer Interface，简称BCI)来实现。64502
本文采用的BCI为神念科技公司开发的“脑立方大脑训练系统”，其硬件包括采用蓝牙串口通信协议的MindWave耳机，可以进行脑波原始数据的提取，借由此信号便可进行进一步的分析与研究。
脑电波波段大致可以划分为Delta、Theta、Alpha、Beta（Low Beta 、Midrange Beta、High Beta）、Gamma五个波段，根据各个波段的能量值便可以简单地识别出部分思维信息。通过MindWave耳机得到原始脑波数据后，本文又完全采用python,以神念科技提供的API为参考，利用人工神经网络算法探索其算法的精确描述，开发了一套基于原始采集数据来识别人的精神集中度与放松度的脑电波分析系统，为以后相关工业产品的设计提供思路和算法指引。
毕业论文关键词：脑电波；人脑思维；串口通信；神经网络；BCI；Python
Research of the state of brain activities based on the EEG analysis
Abstract
Brain wave is biological signals generated by the brain cells. It has been used for diagnosis in diseases of the nervous system, brain function research, etc. since scientists in Germany found the brain signals at the end of the last century 20 s,People want to read people's thoughts or intentions with eeg signals. But eeg signal itself is very weak, its extraction must be through the "Brain machine Interface '(Brain Computer Interface, or BCI).
This paper adopts the BCI for Shennian technology companies to develop "cubic" brain training system, its hardware including the bluetooth serial port communication protocol of the MindWave headset, which can carry out brain waves of raw data extraction. This signal can be used for further analysis and research.
Brain wave band can be roughly pided into the Delta, Theta, Alpha and Beta (Low Beta, Midrange Beta, High Beta), Gamma wavebands, according to the energy of each band can simply identify a part of the thinking information. Raw brain wave data is obtained by MindWave headset, this article completely use python to develope an EEG analysis system using the apis provided by neurosky technology which also use artificial neural network algorithms to explore its accurate description of the algorithm .the concentration and relaxation degree of the spirit can be thus deduced fromthe original data collected by BCI. This will provide ideas and algorithms for the futher design of industrial products.
Keywords:EEG,brain activities, Serial communication, neural network ,BCI,Python
摘 要 I
Abstract II
第一章 引 言 1
第二章 算法概述 2
2.1 基础研究背景 2
2.1.1脑电波频段划分 2
2.1.2 脑电波频段与思维活动关系概述 2
2.2脑波信号预处理 3
2.2.1 综述 3
2.2.2 均值滤波器 3
2.2.3 NOTCH滤波器 5
2.2.4 自适应滤波器设计 5
2.2.5 快速傅里叶变换（FFT） 6
第三章 基于人工神经网络的算法设计