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Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance.See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article. aEuro, Seizures are thought to arise from an identifiable pre-ictal state. Brinkmann et al. report the results of an online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring. The winning algorithms forecast seizures at rates significantly greater than chance.Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 +/- 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.

Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7 to 103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data—a rarity in automated physiological waveform analysis—to hand-chosen features and find that raw data produces comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.

James R. Taylor in the July, 1970 issue of The Slow Learning Child outlined Precision Teaching procedures as they may be used with the handicapped. This article supplements Taylor's, elaborating on two important aspects of Precision Teaching. There is discussion on Precision Teaching in relation to academic behaviour. There is a description of the instrument used in Precision Teaching ‐‐ the six cycle, semi‐logarithmic graph paper. Two Precision Teaching projects are presented using the semi‐logarithmic graph paper.