A
Alexander Craik
Researcher at University of Houston
Publications - 8
Citations - 804
Alexander Craik is an academic researcher from University of Houston. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 3, co-authored 6 publications receiving 321 citations.
Papers
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Journal ArticleDOI
Deep learning for electroencephalogram (EEG) classification tasks: a review.
TL;DR: Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.
Proceedings ArticleDOI
Real-Time Seizure State Tracking Using Two Channels: A Mixed-Filter Approach
Mohammad Badri Ahmadi,Alexander Craik,Hamid Fekri Azgomi,Joseph T. Francis,Jose L. Contreras-Vidal,Rose T. Faghih +5 more
TL;DR: This research performed seizure state detection using a mixed-filter approach to reduce the number of channels and found two optimized EEG features (one binary, one continuous) using wrapper feature selection, making the process more practical and cost-effective.
Journal ArticleDOI
A Mixed Filtering Approach for Real-Time Seizure State Tracking Using Multi-Channel Electroencephalography Data
Alexander G. Steele,Sankalp Parekh,Hamid Fekri Azgomi,Mohammad Badri Ahmadi,Alexander Craik,Sandipan Pati,Joseph T. Francis,Jose L. Contreras-Vidal,Rose T. Faghih +8 more
TL;DR: In this article, the authors make multiple seizure state estimations using a mixed-filter and multiple channels found over the entire sensor space; then by applying a Kalman filter, they produce a single seizure state estimation made up of these individual estimations.
Proceedings ArticleDOI
Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks
TL;DR: The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.