{"title":"脑电图纹状体搏动频率的单周期单频可视化","authors":"J. LaRue","doi":"10.1109/AIPR47015.2019.9174571","DOIUrl":null,"url":null,"abstract":"Motivated by the neuroscience concept of striatal beat frequency a new method is presented that takes an electroencephalogram of brain-wave activity and searches for spectral components using a sliding bank of windows consisting of individual singleperiod single-frequency sinusoids. This new approach is now practical due to the presence of high performance computing architectures. And note, this is a departure from legacy time-frequency spectrogram approaches which use one sliding window of constant size to calculate all frequency components simultaneously and it differs from the wavelet method because a suite of wavelets are formulated around one base unit and that unit usually includes a convolutional ramp up and ramp down structure. The proposed single-period singlefrequency search method is more tangible in identifying the existence, in time, of a frequency component due to its self-imposed constraint of using a bank of windows consisting of single period sinusoids, the length of each being a function of frequency and sampling rate. The result of this approach is a rendering of the on-off nature of the conceptualized striatal beat frequency components through a visualization of a matrix of correlation coefficients, where the rows are individual frequencies, (presented up to 1000 Hz in this paper), and where the columns indicate the position of each detected striatal beat frequency window of time.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Single-Period Single-Frequency (SPSF) Visualization of an EEG’s Striatal Beat Frequency\",\"authors\":\"J. LaRue\",\"doi\":\"10.1109/AIPR47015.2019.9174571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by the neuroscience concept of striatal beat frequency a new method is presented that takes an electroencephalogram of brain-wave activity and searches for spectral components using a sliding bank of windows consisting of individual singleperiod single-frequency sinusoids. This new approach is now practical due to the presence of high performance computing architectures. And note, this is a departure from legacy time-frequency spectrogram approaches which use one sliding window of constant size to calculate all frequency components simultaneously and it differs from the wavelet method because a suite of wavelets are formulated around one base unit and that unit usually includes a convolutional ramp up and ramp down structure. The proposed single-period singlefrequency search method is more tangible in identifying the existence, in time, of a frequency component due to its self-imposed constraint of using a bank of windows consisting of single period sinusoids, the length of each being a function of frequency and sampling rate. The result of this approach is a rendering of the on-off nature of the conceptualized striatal beat frequency components through a visualization of a matrix of correlation coefficients, where the rows are individual frequencies, (presented up to 1000 Hz in this paper), and where the columns indicate the position of each detected striatal beat frequency window of time.\",\"PeriodicalId\":167075,\"journal\":{\"name\":\"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR47015.2019.9174571\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR47015.2019.9174571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Single-Period Single-Frequency (SPSF) Visualization of an EEG’s Striatal Beat Frequency
Motivated by the neuroscience concept of striatal beat frequency a new method is presented that takes an electroencephalogram of brain-wave activity and searches for spectral components using a sliding bank of windows consisting of individual singleperiod single-frequency sinusoids. This new approach is now practical due to the presence of high performance computing architectures. And note, this is a departure from legacy time-frequency spectrogram approaches which use one sliding window of constant size to calculate all frequency components simultaneously and it differs from the wavelet method because a suite of wavelets are formulated around one base unit and that unit usually includes a convolutional ramp up and ramp down structure. The proposed single-period singlefrequency search method is more tangible in identifying the existence, in time, of a frequency component due to its self-imposed constraint of using a bank of windows consisting of single period sinusoids, the length of each being a function of frequency and sampling rate. The result of this approach is a rendering of the on-off nature of the conceptualized striatal beat frequency components through a visualization of a matrix of correlation coefficients, where the rows are individual frequencies, (presented up to 1000 Hz in this paper), and where the columns indicate the position of each detected striatal beat frequency window of time.