Skip to main content

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 ...
Post a Comment

Rhythmic Delta Activity compared to Ocular Artifacts

Image
Dr. Rishabh Pathak

Distinguishing between rhythmic delta activity and ocular artifacts in EEG recordings is crucial for accurate interpretation and diagnosis. Key differences to consider when comparing rhythmic delta activity with ocular artifacts:


1.     Spatial Distribution:

oRhythmic delta activity typically exhibits a widespread distribution across different brain regions, depending on the specific type (e.g., frontal, temporal, occipital).

oIn contrast, ocular artifacts are often localized to frontal or anterior regions due to eye movements or blinks, with minimal involvement of central or posterior areas.

2.   Waveform Characteristics:

oRhythmic delta activity presents as rhythmic, repetitive delta waves with a consistent frequency and morphology, reflecting underlying brain activity or pathology.

oOcular artifacts produce sharp, transient waveforms with distinct contours, reflecting eye movements, blinks, or muscle artifacts that can mimic abnormal EEG patterns.

3.   Temporal Relationship:

oRhythmic delta activity follows a regular pattern of delta waves that may be intermittent or continuous throughout the EEG recording, indicating ongoing brain dysfunction or epileptogenic activity.

oOcular artifacts are typically transient and time-locked to eye movements or blinks, occurring sporadically and ceasing during periods of drowsiness or sleep when the eyes are closed.

4.   Electrode Configuration:

oDifferentiating between rhythmic delta activity and ocular artifacts can be aided by using supraorbital and infraorbital electrodes to assess phase reversals and spatial distribution of potentials.

oOcular artifacts often show phase reversals between infraorbital and supraorbital electrode channels due to the proximity of the electrodes to the eyes, whereas cerebral activity, including rhythmic delta waves, does not exhibit such reversals.

5.    Behavioral Correlates:

oRhythmic delta activity may have specific behavioral correlates, such as seizures, encephalopathies, or structural brain abnormalities, which can help differentiate it from artifacts.

o Ocular artifacts are typically associated with eye movements, blinks, or muscle activity, and their presence may be confirmed by technologist notations or visual inspection of EEG segments.

By considering these distinguishing features and characteristics, healthcare providers can effectively differentiate between rhythmic delta activity and ocular artifacts in EEG recordings, leading to accurate interpretations, appropriate clinical decisions, and improved management of patients with neurological conditions. Integrating knowledge of EEG patterns and artifacts is essential for optimizing diagnostic accuracy and patient care in neurology and clinical neurophysiology settings.

 

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
Post a Comment
Read more

LPFC Functions

Dr. Rishabh Pathak
The lateral prefrontal cortex (LPFC) plays a crucial role in various cognitive functions, particularly those related to executive control, working memory, decision-making, and goal-directed behavior. Here are key functions associated with the lateral prefrontal cortex: 1.      Executive Functions : o     The LPFC is central to executive functions, which encompass higher-order cognitive processes involved in goal setting, planning, problem-solving, cognitive flexibility, and inhibitory control. o     It is responsible for coordinating and regulating other brain regions to support complex cognitive tasks, such as task switching, attentional control, and response inhibition, essential for adaptive behavior in changing environments. 2.      Working Memory : o     The LPFC is critical for working memory processes, which involve the temporary storage and manipulation of information to guide behavior and decis...
Post a Comment
Read more

Supervised Learning

Dr. Rishabh Pathak
What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...
Post a Comment
Read more