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04:08The Uncertainty Principle
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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02:46Uncertainty in Measurement: Reading Instruments
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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00:59Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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03:34Uncertainty in Measurement: Significant Figures
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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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00:54Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty in deep learning for EEG under dataset shifts.
Mats Tveter1, Thomas Tveitstøl1, Christoffer Hatlestad-Hall2
1Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
Artificial Intelligence in Medicine
|February 6, 2026
Summary
Ensemble learning methods improve artificial intelligence (AI) diagnostic accuracy and uncertainty estimation for electroencephalography (EEG) data. These AI models reliably predict cognitive decline, even with data shifts, enhancing clinical trust.
Area of Science:
- Medical Diagnostics
- Artificial Intelligence
- Neuroscience
Background:
- Integrating AI into medical diagnostics requires reliable uncertainty estimates alongside accurate predictions.
- Understanding prediction confidence is crucial for clinical decision-making, especially in out-of-distribution scenarios.
- Uncertainty metrics aim to align model confidence with actual performance, adapting to reliability changes.
Purpose of the Study:
- To investigate the impact of ensemble learning strategies on performance and uncertainty estimation in EEG-based cognitive decline classification.
- To evaluate AI models on in-distribution, out-of-distribution, and dataset-shifted EEG data.
- To assess the reliability of uncertainty estimates under various data perturbations.
Main Methods:
- Evaluated ensemble methods and Monte Carlo dropout on a large EEG dataset.
- Assessed model performance and uncertainty in three settings: in-distribution, out-of-distribution generalization, and gradual dataset shifts (noise, drift, frequency perturbation).
- Utilized deep ensembles and independently trained models for robust classification.
Main Results:
- Ensemble methods, particularly deep ensembles, consistently outperformed other models in both in-distribution and out-of-distribution settings.
- Ensembles provided more informative and reliable uncertainty estimates across different types of EEG dataset shifts.
- Model diversity and independent training proved beneficial for robust, uncertainty-aware classification.
Conclusions:
- Ensemble learning strategies enhance the reliability and accuracy of AI models for EEG-based cognitive decline detection.
- These findings support the clinical deployment of AI by ensuring transparency and robustness under data variability.
- Uncertainty-aware models are essential for safe and trustworthy AI applications in healthcare.