Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: article i nfo Computing phase-locking values beten EEG signals is a popular method for quantifying functional connectivity. Hover, this method involves large-scale, high-resolution datasets, which impose a serious multiple testing problem. Standard multiple testing methods fail to exploit the information from the complex dependence structure that varies across hypotheses in spectral, temporal, and spatial dimensions and result in a severe loss of por. They tend to control the false positives at the cost of hiding true positives.  introduce a new approach, called optimal discovery procedure (ODP) for identifying synchrony that is statistically significant. ODP maximizes the number of true positives for a given number of false positives, and thus offers a theoretical optimum for detecting significant synchrony in a multiple testing situation.  demonstrate the utility of this method with PLV data obtained from a visual search study.  also present simulation analysis to confirm the validity and relevance of using ODP in comparison with the standard FDR method for given configurations of true synchrony.  also compare the effectiveness of ODP with our previously published investigation of hierarchical FDR method (Singh and Phillips, 2010).
['Archana K. Singh', 'Hideki Asoh', 'Steven Phillips']
I am writing a research paper on a idea Optimal detection of functional connectivity from high-dimensional EEG synchrony data.
[{"from": "human", "value": "I am writing a research paper on a idea Optimal detection of functional connectivity from high-dimensional EEG synchrony data."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: article i nfo Computing phase-locking values beten EEG signals is a popular method for quantifying functional connectivity. Hover, this method involves large-scale, high-resolution datasets, which impose a serious multiple testing problem. Standard multiple testing methods fail to exploit the information from the complex dependence structure that varies across hypotheses in spectral, temporal, and spatial dimensions and result in a severe loss of por. They tend to control the false positives at the cost of hiding true positives.  introduce a new approach, called optimal discovery procedure (ODP) for identifying synchrony that is statistically significant. ODP maximizes the number of true positives for a given number of false positives, and thus offers a theoretical optimum for detecting significant synchrony in a multiple testing situation.  demonstrate the utility of this method with PLV data obtained from a visual search study.  also present simulation analysis to confirm the validity and relevance of using ODP in comparison with the standard FDR method for given configurations of true synchrony.  also compare the effectiveness of ODP with our previously published investigation of hierarchical FDR method (Singh and Phillips, 2010)."}]