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EEG Feature Extraction in Brain-Mobile PhoneInterfaces

机译:脑-手机接口中的脑电特征提取

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Aims: The paper describes four methods for extracting features of brain signals in frequency and time domain that can be used as parameters for identifying the face images of people.Place and Duration of Study: Human Computer Interface Lab, Karpagam University, India (July 2012-June 2014)Methodology: The subject is asked to remember different known face images like father, mother and so on and the corresponding Electroencephalogram (EEG) signals are captured. Wigner Ville Distribution and other spectral methods are used for studying the features. The data was collected from 15 subjects having good mental health condition. Real EEG records from different subjects are taken for duration of 10 seconds in each trial. 10 such trials are taken from each test subject.Results: It is noticed that the band of frequencies in the range 0-40 Hz shows higher spectral variations, due to “remembering” or retrieving memories possibly due to the presence of Alpha or Beta waves. Using Fourier spectrum analysis it is found that, the EEG signals corresponding to the face image of one person (e.g: Mother) was always giving a different range of values for the number of spectral crossings, in comparison to the second face image (eg: Father). During the Wigner Ville analysis the peak value of instantaneous power in one case was seen in the range of 2x10~(5) to 3x10~(5 )for father’s face whereas for mother’s face in the range of 0.5x10~(6) to 1x10~(6). In the Power Spectral Density based analysis, the frequency range of 10-20 Hz showed a higher average value in case of mother’s face than in father’s. When the mean signal power was calculated from PSD for different trials, it is noticed that the signal power is significantly different in cases of father and mother and gavea 70-90% of correct classification result.Conclusion: After identifying the features that are unique for a face image, the same is proposed to be used for the address book dialing in a smart phone which can be further used for helping physically disabled as well as normal people to interact with external world.
机译:目的:本文描述了四种在频域和时域中提取脑信号特征的方法,这些方法可以用作识别人脸图像的参数。研究的地点和持续时间:印度卡尔帕甘大学人机界面实验室(2012年7月) -2014年6月)方法:要求受试者记住不同的已知面部图像,例如父亲,母亲等,并捕获相应的脑电图(EEG)信号。 Wigner Ville分布和其他光谱方法用于研究特征。数据是从15名心理健康状况良好的受试者中收集的。在每个试验中,将不同受试者的真实脑电图记录持续10秒。每个测试对象进行了10次这样的试验。结果:注意到0-40 Hz范围内的频带显示出较高的频谱变化,这是由于“记忆”或检索到的记忆(可能是由于存在Alpha或Beta波) 。使用傅里叶频谱分析发现,与第二个人脸图像(例如:)相比,与一个人(例如母亲)的脸部图像相对应的EEG信号始终在光谱交叉数量上给出不同范围的值。父亲)。在Wigner Ville分析过程中,一种情况下,父亲的脸部瞬时功率峰值在2x10〜(5)至3x10〜(5)范围内,而母亲的脸部则在0.5x10〜(6)至1x10范围内。 〜(6)。在基于功率谱密度的分析中,母亲脸部情况下的10-20 Hz频率范围显示出高于父亲的平均值。当从PSD计算出不同试验的平均信号功率时,可以注意到,在父母和母亲给予正确分类结果的70-90%的情况下,信号功率显着不同。结论:确定了独特的特征后对于面部图像,建议将其用于智能手机中的地址簿拨号,该图像可进一步用于帮助肢体残障人士以及普通人与外界互动。

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