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<h2>Welcome to MODA</h2> <p>A high quality annotated set of spindles.</p> <p>For a description of the data, go to <a href="https://osf.io/8bma7/wiki/About%20the%20Data/" rel="nofollow">About the Data</a></p> <p>For the available format, and to download, go to <a href="https://osf.io/8bma7/wiki/Download/" rel="nofollow">Download</a></p> <h2>Current Status</h2> <p><strong>2018-02-01</strong> : MODA interface scoring platform available (<a href="http://github.com/bdyetton/MODA" rel="nofollow">github.com/bdyetton/MODA</a>).</p> <p><strong>2019-02-01</strong> : Spindle scoring completed (phase 1 & 2). </p> <p><strong>2019-10-01</strong> : Gold standard spindle data set available (<a href="http://github.com/klacourse/MODA_GC" rel="nofollow">github.com/klacourse/MODA_GC</a>).</p> <h2>Change Log</h2> <p>v0.1: First release</p> <p>v0.2: Added Confidence for each marker</p> <p>v0.3: Updated the wiki and the data download with the complete data set.</p> <h2>What is the context of this research?</h2> <p>Polysomnography, which includes electroencephalography (EEG), is a common technique used to measure brainwaves during sleep. It has become increasingly apparent that features of this EEG signal such as sleep spindles, Rapid Eye Movements (REMs) and k-complexes play an important biological and psychological role in sleep. Traditionally expert humans judgement has been used to detect these features; however automated feature detection tools are increasingly used. Due to the generally small and lower validity datasets currently used in tool development, many published detectors have not undergone rigorous validation techniques and often perform poorly in practice. These automatic detectors must be improved or other detection methods must be employed.</p> <p>In this project we ask non-expert Mechanical Turk Workers, as well as expert sleep technicians and researchers to detect spindles in a large polysomnography dataset. </p> <p>The data collected from is freely available and will serve as a 'gold standard' to allow engineers and others to develop higher quality sleep feature detection tools. This will help us understand how sleep plays a role in memory and aid the detection of mental disorders. While these tools will not be able to replace human expertise, they will be extremely valuable for the analysis of large datasets where human analysis alone is not feasible.</p>
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