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E. Koprivanacz
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Visualizing EEG data in cloud environments
How to downsample large EEG signals, keeping clinically relevant waveforms while minimizing end-to-end latency
Electroencephalography is a widely used non-invasive technique used for measuring brain activity. While relatively cheap, the large volumes of data it produces can make analysis and visualization challenging. NBT Cloud aims to address these challenges by bringing EEG analysis to the cloud and creating an ecosystem for standardized, real-time analysis. Because the application must visualize large data in real time, downsampling the signals is necessary. Modern EEG analysis toolkits, however, focus on downsampling while keeping the data's statistical properties, which introduces computational overhead. This paper investigates whether other downsampling methods, such as Min-Max or Largest Triangle Three Buckets, can achieve better visual fidelity at lower runtime. The results show that simpler candidate algorithms better preserve the visual characteristics of EEG signals while achieving lower runtimes. Among the evaluated algorithms, Min-Max offers the best trade-off between visual similarity and performance, making it the most suitable choice for the use case of NBT Cloud.
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Electroencephalography is a widely used non-invasive technique used for measuring brain activity. While relatively cheap, the large volumes of data it produces can make analysis and visualization challenging. NBT Cloud aims to address these challenges by bringing EEG analysis to the cloud and creating an ecosystem for standardized, real-time analysis. Because the application must visualize large data in real time, downsampling the signals is necessary. Modern EEG analysis toolkits, however, focus on downsampling while keeping the data's statistical properties, which introduces computational overhead. This paper investigates whether other downsampling methods, such as Min-Max or Largest Triangle Three Buckets, can achieve better visual fidelity at lower runtime. The results show that simpler candidate algorithms better preserve the visual characteristics of EEG signals while achieving lower runtimes. Among the evaluated algorithms, Min-Max offers the best trade-off between visual similarity and performance, making it the most suitable choice for the use case of NBT Cloud.