SMR neurofeedback for improving sleep and memory – Two studies in primary insomnia

Abstract

ming and subject reporting errors. Unlike with traditional data collection, error identification required a systematic, non-qualitative approach. A method for discerning between errors and outliers was also necessary. However, these challenges were met through developing a pattern detection approach within single and multiple fields. In addition, because of the sheer quantity of errors and outliers, they could be analyzed to identify trends to both promote data quality and to answer new, substantive questions of the data. For example, benefits included the possibility of categorizing types of users through patterns of responses, and the identification of learning curves for diary entry through patterns of errors. Other improvements to data quality and analysis included reducedmissing data, improved data integrity, and substantial increases in data capture. Conclusion: The development of newmethodologies for addressing insomnia-specific issues in the automated, large scale collection of data is needed in the age of Big Data trials. We propose methods for addressing data quality andmanagement issues, and discuss possible new directions in the examination of insomnia data. Acknowledgements: Funding for this study came from the National Institutes of Health/National Institute of Mental Health (R01MH086758).