By early spring 2020, the COVID-19 pandemic caused mandatory campus closures of academic institutions nationwide, prompting the rapid transition to online instruction. While lectures and exams were more straightforwardly administered online using video-chatting software, many hands-on laboratory-based courses were forced to develop creative solutions. In response to online instructional requirements, instructors at the University of California Irvine developed an online electroencephalography (EEG) laboratory to simulate the laboratory experiment for students unable to perform the experiment on campus. The laboratory experiment was performed and video recorded by the instructional team under three different scenarios to provide students with multiple data sets acquired under various experimental conditions often enacted by students. Students were required to complete a pre-lab quiz, analyze the acquired EEG data offline, complete a post-lab quiz, and submit their laboratory report to communicate their findings prior to final exams. Student performances compared to prior student performances, and qualitative survey responses, were examined to assess the effectiveness of and response to the online laboratory format. Based on student feedback and lab report grades, the majority of students responded positively and demonstrated an understanding of the EEG experiment’s learning outcomes. In summary, the online EEG laboratory enabled students to achieve the main learning objectives and become familiar with the laboratory experiment, indicating its success as an alternative laboratory experiment. Electronic supplementary material The online version of this article (10.1007/s43683-020-00034-9) contains supplementary material, which is available to authorized users.

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High frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.