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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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Perspiration Artifacts

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Dr. Rishabh Pathak

Perspiration artifacts, also known as sweat artifacts, are a type of artifact that can affect EEG recordings. 


1.     Description:

oNature: Perspiration artifacts result from the presence of sweat on the scalp, leading to changes in electrical conductivity and impedance that affect EEG signals.

oAppearance: These artifacts manifest as high-amplitude and low-frequency activity primarily across bilateral frontal and temporal regions, reflecting the influence of sweat on electrode readings.

oWaveform: Perspiration artifacts exhibit characteristic waveforms that are typical for sweat artifacts, with specific patterns and distributions across the scalp.

oLocalization: The impact of perspiration artifacts is often widespread, affecting multiple electrodes and regions due to the diffusion of sweat.

2.   Causes:

oFactors: Perspiration artifacts are caused by the presence of sweat on the scalp, which alters the electrical properties of the skin and electrode interface.

oEffect: Changes in electrical conductivity due to sweat can lead to distortions in EEG signals, affecting the interpretation of brain activity.

3.   Differentiation:

oDistinct Characteristics: Perspiration artifacts have specific waveform and distribution patterns that differentiate them from other types of artifacts or genuine EEG activity.

o Field Presence: These artifacts typically exhibit a consistent field across bilateral frontal and temporal regions, reflecting their origin from sweat on the scalp.

4.   Recognition:

oVisual Cue: The high-amplitude and low-frequency activity across specific regions, along with the typical waveform, serves as a visual cue for identifying perspiration artifacts in EEG recordings.

oConfirmation: Observing the persistence of artifact patterns despite changes in background activity can help confirm the presence of sweat artifacts in EEG data.

Understanding the characteristics and effects of perspiration artifacts is essential for EEG technicians and clinicians to recognize and address these disturbances during EEG recording and analysis. Proper management of sweat artifacts contributes to the accuracy and reliability of EEG data interpretation in clinical and research settings.

 

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  1. SRIJuly 10, 2024 at 2:03 AM

    Please Share your social media or e-mail to contact you @ Dr. Rishabh. Have you ever worked in the Brain Computer Interface ? These insight are directing us towards understanding of the basics for Brain Computer Interface. Please do share your Contact detail. Regards.

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