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{
"title": "Independent Component Analysis Mastery: 100 MCQs",
"description": "A comprehensive set of 100 multiple-choice questions to test and deepen your understanding of ICA, covering fundamentals, assumptions, applications, and practical scenarios.",
"questions": [
{
"id": 1,
"questionText": "What is the main goal of Independent Component Analysis (ICA)?",
"options": [
"To separate a multivariate signal into additive independent components",
"To predict a continuous target variable",
"To cluster similar data points",
"To reduce the dimensionality of data"
],
"correctAnswerIndex": 0,
"explanation": "ICA aims to decompose mixed signals into statistically independent components, often used in blind source separation."
},
{
"id": 2,
"questionText": "Which assumption is crucial for ICA?",
"options": [
"All features are equally scaled",
"Components are statistically independent and non-Gaussian",
"Components are Gaussian",
"Data has no missing values"
],
"correctAnswerIndex": 1,
"explanation": "ICA requires that the underlying sources be statistically independent and non-Gaussian to successfully separate them."
},
{
"id": 3,
"questionText": "ICA is commonly applied in:",
"options": [
"Predicting stock prices",
"Image recognition only",
"Dimensionality reduction only",
"Blind source separation, like separating mixed audio signals"
],
"correctAnswerIndex": 3,
"explanation": "ICA is widely used for separating mixed signals, such as audio, EEG, and financial signals, where independence is assumed."
},
{
"id": 4,
"questionText": "Scenario: You mix two audio signals into two recordings. Applying ICA:",
"options": [
"Will reduce dimensions only",
"Will cluster the recordings",
"Cannot do anything without labels",
"Can recover the original separate audio sources"
],
"correctAnswerIndex": 3,
"explanation": "ICA can separate mixed signals into the original independent sources, assuming statistical independence."
},
{
"id": 5,
"questionText": "Which property differentiates ICA from PCA?",
"options": [
"ICA finds independent components, PCA finds uncorrelated components",
"ICA reduces dimensions, PCA does not",
"ICA works only for Gaussian data",
"PCA requires independence, ICA does not"
],
"correctAnswerIndex": 0,
"explanation": "PCA decorrelates data but does not ensure independence. ICA focuses on statistical independence of components."
},
{
"id": 6,
"questionText": "Scenario: You apply ICA on 3 mixed signals but get more than 3 components. Likely reason?",
"options": [
"Algorithm error or wrong number of components specified",
"Random initialization causes extra components",
"Mixing is linear, so components must increase",
"ICA always produces more components"
],
"correctAnswerIndex": 0,
"explanation": "The number of extracted independent components should not exceed the number of observed mixtures."
},
{
"id": 7,
"questionText": "ICA assumes the mixing process is:",
"options": [
"Nonlinear only",
"Non-invertible",
"Randomly noisy",
"Linear and invertible"
],
"correctAnswerIndex": 3,
"explanation": "Standard ICA assumes the observed signals are linear mixtures of independent sources, which allows recovery."
},
{
"id": 8,
"questionText": "Scenario: You apply ICA on EEG data. Purpose?",
"options": [
"Remove artifacts like eye blinks",
"Reduce dimensionality only",
"Cluster subjects",
"Predict disease directly"
],
"correctAnswerIndex": 0,
"explanation": "ICA can separate EEG components and isolate artifacts for cleaner signal analysis."
},
{
"id": 9,
"questionText": "Which metric is commonly used to measure independence in ICA?",
"options": [
"Euclidean distance",
"Correlation coefficient",
"Variance explained",
"Kurtosis or mutual information"
],
"correctAnswerIndex": 3,
"explanation": "Non-Gaussianity measures like kurtosis or mutual information are used to quantify statistical independence."
},
{
"id": 10,
"questionText": "Scenario: Two independent sources are Gaussian. Applying ICA?",
"options": [
"Separation will work perfectly",
"ICA will automatically decorrelate",
"Cannot separate them because Gaussian sources do not satisfy ICA assumptions",
"PCA is better"
],
"correctAnswerIndex": 2,
"explanation": "ICA requires non-Gaussian sources; Gaussian independent sources cannot be separated due to rotational ambiguity."
},
{
"id": 11,
"questionText": "Scenario: You observe mixed signals from two microphones. ICA aims to:",
"options": [
"Cluster the microphone locations",
"Predict the next sound sample",
"Reduce noise only",
"Separate the original sound sources"
],
"correctAnswerIndex": 3,
"explanation": "ICA separates mixed signals into their statistically independent source components."
},
{
"id": 12,
"questionText": "What type of data scaling is usually recommended before ICA?",
"options": [
"Normalization to [0,1]",
"Centering and whitening",
"Log transformation",
"No scaling needed"
],
"correctAnswerIndex": 1,
"explanation": "Centering (zero mean) and whitening (decorrelation) improve ICA performance."
},
{
"id": 13,
"questionText": "Scenario: ICA applied on two mixed images. Output?",
"options": [
"Generate random noise",
"Compress the images",
"Recover original independent images",
"Reduce image resolution"
],
"correctAnswerIndex": 2,
"explanation": "ICA can separate mixed signals in images, like separating overlapping patterns."
},
{
"id": 14,
"questionText": "ICA works best when sources are:",
"options": [
"Gaussian",
"Non-Gaussian and independent",
"Highly correlated",
"Categorical"
],
"correctAnswerIndex": 1,
"explanation": "ICA relies on non-Gaussianity and independence to separate components successfully."
},
{
"id": 15,
"questionText": "Scenario: Two audio signals mixed linearly, ICA extracts 2 components. Issue if you extract 3?",
"options": [
"Extra component is meaningless",
"Algorithm improves accuracy",
"Signals become correlated",
"Automatically reduces to 2"
],
"correctAnswerIndex": 0,
"explanation": "Number of extracted components should match observed mixtures; extra components do not correspond to real sources."
},
{
"id": 16,
"questionText": "ICA differs from PCA because:",
"options": [
"ICA reduces dimensions, PCA does not",
"PCA finds independent components, ICA finds uncorrelated components",
"PCA decorrelates, ICA seeks independence",
"ICA is supervised"
],
"correctAnswerIndex": 2,
"explanation": "PCA removes correlation, ICA removes higher-order dependencies (statistical independence)."
},
{
"id": 17,
"questionText": "Scenario: You want to denoise images using ICA. How?",
"options": [
"Randomly remove pixels",
"Apply PCA only",
"Cluster similar images",
"Separate noise components from signals and remove them"
],
"correctAnswerIndex": 3,
"explanation": "ICA can isolate noise as an independent component, enabling its removal."
},
{
"id": 18,
"questionText": "Scenario: Applying ICA on mixed financial time series. Goal?",
"options": [
"Reduce time resolution",
"Predict exact future prices",
"Extract independent latent factors affecting markets",
"Cluster assets"
],
"correctAnswerIndex": 2,
"explanation": "ICA identifies underlying independent sources driving observed mixed signals."
},
{
"id": 19,
"questionText": "ICA requires which property of the mixing matrix?",
"options": [
"Diagonal",
"Singular",
"Invertible",
"Random"
],
"correctAnswerIndex": 2,
"explanation": "The mixing matrix must be invertible to recover the original sources."
},
{
"id": 20,
"questionText": "Scenario: Two signals are perfectly Gaussian. ICA outcome?",
"options": [
"Separation works normally",
"Cannot separate sources due to rotational ambiguity",
"Algorithm produces errors",
"Signals are automatically decorrelated"
],
"correctAnswerIndex": 1,
"explanation": "ICA cannot separate Gaussian sources as any orthogonal rotation preserves Gaussianity."
},
{
"id": 21,
"questionText": "ICA can be used in which biomedical application?",
"options": [
"Genetic sequencing",
"X-ray imaging only",
"Blood pressure measurement",
"EEG artifact removal"
],
"correctAnswerIndex": 3,
"explanation": "ICA is commonly used to separate eye-blink and muscle artifacts from EEG recordings."
},
{
"id": 22,
"questionText": "Scenario: ICA on audio + noise mixture. Noise is independent. Outcome?",
"options": [
"Noise can be separated and removed",
"Signals become correlated",
"Noise remains mixed",
"Cannot separate due to Gaussian assumption"
],
"correctAnswerIndex": 0,
"explanation": "ICA can isolate independent noise components for removal."
},
{
"id": 23,
"questionText": "Scenario: ICA applied to images with overlapping letters. Goal?",
"options": [
"Predict next letter",
"Separate individual letter images",
"Remove color information",
"Compress the image"
],
"correctAnswerIndex": 1,
"explanation": "ICA separates mixed patterns into independent sources, such as letters overlapping in images."
},
{
"id": 24,
"questionText": "Which algorithm is commonly used for ICA?",
"options": [
"K-means",
"SVM",
"FastICA",
"Decision Tree"
],
"correctAnswerIndex": 2,
"explanation": "FastICA is a popular algorithm that maximizes non-Gaussianity to find independent components."
},
{
"id": 25,
"questionText": "Scenario: You mix 3 audio signals. Observed signals = 3. How many ICs can you extract?",
"options": [
"More than 3",
"1",
"At most 3",
"Cannot extract any"
],
"correctAnswerIndex": 2,
"explanation": "Number of independent components cannot exceed number of observed mixtures."
},
{
"id": 26,
"questionText": "Scenario: ICA applied to sensor signals with outliers. Best practice?",
"options": [
"Increase dimensions",
"Ignore outliers",
"Preprocess or remove outliers before ICA",
"Randomly mix signals"
],
"correctAnswerIndex": 2,
"explanation": "Outliers distort estimated independent components; preprocessing improves performance."
},
{
"id": 27,
"questionText": "Scenario: ICA on financial returns data. Why non-Gaussianity is needed?",
"options": [
"Independence does not matter",
"Gaussian sources are easier",
"Gaussian data cannot be analyzed",
"Non-Gaussianity ensures sources are uniquely recoverable"
],
"correctAnswerIndex": 3,
"explanation": "ICA leverages higher-order statistics of non-Gaussian sources for unique separation."
},
{
"id": 28,
"questionText": "Scenario: ICA applied on images of faces. Use case?",
"options": [
"Identify independent facial features",
"Predict identity directly",
"Compress images",
"Cluster faces only"
],
"correctAnswerIndex": 0,
"explanation": "ICA can extract independent features like eyes, nose, mouth patterns for face recognition."
},
{
"id": 29,
"questionText": "Scenario: Whitening is done before ICA. Why?",
"options": [
"Random initialization",
"Reduces dimensionality only",
"Reduces correlation and simplifies component extraction",
"Removes labels"
],
"correctAnswerIndex": 2,
"explanation": "Whitening transforms data to uncorrelated components, improving ICA convergence."
},
{
"id": 30,
"questionText": "Scenario: ICA applied to music mixture. Output components are rotated. Why?",
"options": [
"Algorithm failed",
"Features are missing",
"ICA is unique up to scaling and permutation",
"Data is Gaussian"
],
"correctAnswerIndex": 2,
"explanation": "ICA components are identifiable only up to scaling and order; rotation/permutation does not affect independence."
},
{
"id": 31,
"questionText": "Scenario: ICA applied to mixed EEG signals. You observe one component is dominated by eye-blink artifacts. Best action?",
"options": [
"Keep all components",
"Apply PCA only",
"Remove that component to clean EEG",
"Randomly select another component"
],
"correctAnswerIndex": 2,
"explanation": "ICA separates independent sources; removing artifact-dominated components cleans the EEG signal."
},
{
"id": 32,
"questionText": "ICA assumes that the sources are:",
"options": [
"Correlated and Gaussian",
"Non-Gaussian and statistically independent",
"Categorical only",
"Binary and independent"
],
"correctAnswerIndex": 1,
"explanation": "ICA relies on non-Gaussianity and independence to successfully separate mixed signals."
},
{
"id": 33,
"questionText": "Scenario: Two mixed audio signals, one is nearly Gaussian. Applying ICA?",
"options": [
"Algorithm automatically converts to non-Gaussian",
"Separation works perfectly",
"May not separate Gaussian source",
"Ignore the Gaussian source"
],
"correctAnswerIndex": 2,
"explanation": "Gaussian sources cannot be uniquely separated due to rotational ambiguity in ICA."
},
{
"id": 34,
"questionText": "Scenario: You apply ICA on financial time series. One extracted component shows sudden spikes. Likely reason?",
"options": [
"Independent shock or outlier in market data",
"Gaussian assumption violated",
"Algorithm failure",
"Too few observations"
],
"correctAnswerIndex": 0,
"explanation": "ICA separates independent sources; sudden spikes may correspond to independent events or outliers."
},
{
"id": 35,
"questionText": "ICA can be combined with PCA. Why?",
"options": [
"PCA improves independence",
"Reduce dimensionality and noise before applying ICA",
"Only for visualization",
"ICA replaces PCA"
],
"correctAnswerIndex": 1,
"explanation": "PCA whitening simplifies ICA computation and reduces noise in high-dimensional data."
},
{
"id": 36,
"questionText": "Scenario: ICA applied to two mixed audio signals. Number of sources = number of observations. What if more sources than observations?",
"options": [
"ICA works normally",
"Cannot fully recover sources",
"Ignore extra sources",
"Extra sources merged automatically"
],
"correctAnswerIndex": 1,
"explanation": "ICA requires the number of observed mixtures ≥ number of sources for unique recovery."
},
{
"id": 37,
"questionText": "ICA maximizes:",
"options": [
"Variance explained",
"Non-Gaussianity of components",
"Correlation between signals",
"Euclidean distance"
],
"correctAnswerIndex": 1,
"explanation": "ICA algorithms maximize non-Gaussianity (kurtosis, negentropy) to find independent components."
},
{
"id": 38,
"questionText": "Scenario: ICA on images with overlapping text. Extracted component is noisy. Solution?",
"options": [
"Apply PCA only",
"Remove components randomly",
"Preprocess images, apply filtering, then ICA",
"Increase number of components"
],
"correctAnswerIndex": 2,
"explanation": "Noise can be reduced by preprocessing before ICA for clearer separation."
},
{
"id": 39,
"questionText": "Scenario: You use ICA for blind source separation of mixed speech signals. One component is silent. Likely cause?",
"options": [
"Gaussian assumption violated",
"Random initialization failed",
"Algorithm error",
"ICA extracted a component with very low contribution from sources"
],
"correctAnswerIndex": 3,
"explanation": "ICA may extract components with negligible variance, appearing silent but still independent."
},
{
"id": 40,
"questionText": "Scenario: ICA applied to EEG, but one channel shows mixture of multiple brain regions. Why?",
"options": [
"Algorithm failed",
"Signal is mixed; ICA separates independent sources, but spatial resolution limited",
"Data is Gaussian",
"Channel is corrupted"
],
"correctAnswerIndex": 1,
"explanation": "ICA can separate sources, but physical sensor overlap may cause mixed contributions."
},
{
"id": 41,
"questionText": "Scenario: ICA applied on high-dimensional dataset. Observed singular matrix. Solution?",
"options": [
"Ignore issue",
"Apply PCA for dimensionality reduction before ICA",
"Increase output dimensions",
"Remove random features"
],
"correctAnswerIndex": 1,
"explanation": "High-dimensional data can cause singular covariance; PCA reduces dimensions and stabilizes ICA."
},
{
"id": 42,
"questionText": "Scenario: ICA applied to audio signals with strong noise. Best approach?",
"options": [
"Reduce number of components",
"Increase ICA iterations",
"Preprocess to reduce noise or apply filtering",
"Use raw signals"
],
"correctAnswerIndex": 2,
"explanation": "Noise affects ICA separation; preprocessing improves quality."
},
{
"id": 43,
"questionText": "Scenario: ICA applied on two mixed images; one image is highly uniform. Effect?",
"options": [
"Algorithm automatically enhances it",
"ICA may have difficulty separating low-variance components",
"No effect",
"Outputs random component"
],
"correctAnswerIndex": 1,
"explanation": "Low-variance sources contribute little to the mixture, making separation challenging."
},
{
"id": 44,
"questionText": "Scenario: ICA applied to mixed sensor signals, some channels missing. Effect?",
"options": [
"Data automatically interpolated",
"ICA works normally",
"Algorithm generates random values",
"Cannot fully recover sources"
],
"correctAnswerIndex": 3,
"explanation": "Missing observations reduce information; ICA cannot recover all independent components."
},
{
"id": 45,
"questionText": "Scenario: ICA on audio and image data combined. Feasible?",
"options": [
"Only images can be separated",
"No, ICA works only for audio",
"Yes, if signals are mixed and independent",
"Only if data is Gaussian"
],
"correctAnswerIndex": 2,
"explanation": "ICA separates independent components regardless of domain, provided assumptions hold."
},
{
"id": 46,
"questionText": "Scenario: ICA applied to EEG signals. A component contains mixed artifacts. Why?",
"options": [
"Artifacts may not be perfectly independent",
"Gaussian assumption violated",
"Number of components too high",
"Algorithm error"
],
"correctAnswerIndex": 0,
"explanation": "Non-perfect independence of sources may cause mixed artifact components."
},
{
"id": 47,
"questionText": "ICA vs PCA: Which captures higher-order statistics?",
"options": [
"PCA",
"ICA",
"Neither",
"Both equally"
],
"correctAnswerIndex": 1,
"explanation": "ICA uses higher-order statistics (non-Gaussianity), while PCA relies only on covariance (second-order statistics)."
},
{
"id": 48,
"questionText": "Scenario: ICA applied on two mixed audio tracks. Output shows small artifacts. Best practice?",
"options": [
"Discard ICA result",
"Randomly re-initialize algorithm",
"Increase number of components",
"Post-process with filtering or denoising"
],
"correctAnswerIndex": 3,
"explanation": "Post-processing can clean residual artifacts after ICA separation."
},
{
"id": 49,
"questionText": "Scenario: ICA applied to financial signals. One component is highly skewed. Why?",
"options": [
"ICA extracts non-Gaussian independent components, skewed distributions are typical",
"Noise corrupted data",
"Algorithm failed",
"Increase number of components"
],
"correctAnswerIndex": 0,
"explanation": "ICA maximizes non-Gaussianity; skewed components are expected and represent independent sources."
},
{
"id": 50,
"questionText": "Scenario: ICA applied on audio signals. Components randomly scaled. Why?",
"options": [
"Algorithm failed",
"Number of components wrong",
"Data is Gaussian",
"ICA components are identifiable up to scaling and permutation"
],
"correctAnswerIndex": 3,
"explanation": "ICA cannot determine original amplitude; scaling ambiguity is inherent in ICA."
},
{
"id": 51,
"questionText": "Scenario: ICA applied to two mixed images, one component inverted. Reason?",
"options": [
"Gaussian assumption violated",
"ICA components are determined up to sign (polarity) ambiguity",
"Algorithm error",
"Noise dominance"
],
"correctAnswerIndex": 1,
"explanation": "Sign ambiguity is common in ICA; independent components may appear inverted but remain valid."
},
{
"id": 52,
"questionText": "Scenario: You want to reduce dimensionality but retain independent features. Strategy?",
"options": [
"Apply PCA only",
"Combine PCA for whitening, then ICA",
"Apply ICA only",
"Randomly remove features"
],
"correctAnswerIndex": 1,
"explanation": "Whitening via PCA reduces dimensionality and removes correlations, improving ICA performance."
},
{
"id": 53,
"questionText": "Scenario: ICA applied to mixed music recordings. Some components overlap in frequency. Effect?",
"options": [
"Algorithm fails entirely",
"No effect",
"Partial separation; ICA may not fully disentangle overlapping frequency bands",
"Complete separation"
],
"correctAnswerIndex": 2,
"explanation": "ICA separates independent sources; overlapping frequency bands can reduce separation quality."
},
{
"id": 54,
"questionText": "Scenario: ICA applied to EEG with eye blink artifacts. Component shows partial overlap with brain signals. Action?",
"options": [
"Carefully remove or attenuate artifact component to avoid losing brain signal",
"Keep all components",
"Remove completely",
"Apply PCA only"
],
"correctAnswerIndex": 0,
"explanation": "Overlapping components may contain both artifact and signal; selective attenuation preserves information."
},
{
"id": 55,
"questionText": "Scenario: ICA applied on audio mixture; one speaker quiet. Component extracted is faint. Why?",
"options": [
"Algorithm failed",
"Source contribution is low, reflected in component magnitude",
"Gaussian assumption violated",
"Random initialization"
],
"correctAnswerIndex": 1,
"explanation": "Components magnitude reflects source contribution; faint signals indicate weak source presence."
},
{
"id": 56,
"questionText": "ICA is particularly useful when signals are:",
"options": [
"Nonlinear only",
"Gaussian and correlated",
"Linearly mixed and non-Gaussian",
"Categorical"
],
"correctAnswerIndex": 2,
"explanation": "ICA assumes linear mixing and non-Gaussian independent sources for successful separation."
},
{
"id": 57,
"questionText": "Scenario: ICA on mixed images produces components rotated. Reason?",
"options": [
"Algorithm error",
"Data too noisy",
"ICA components are identifiable only up to rotation, scaling, and permutation",
"Gaussian assumption violated"
],
"correctAnswerIndex": 2,
"explanation": "Rotation ambiguity is inherent; components may appear rotated but remain valid independent sources."
},
{
"id": 58,
"questionText": "Scenario: ICA applied to noisy EEG signals. Preprocessing includes:",
"options": [
"Removing labels only",
"Random sampling",
"Centering, whitening, artifact filtering",
"No preprocessing"
],
"correctAnswerIndex": 2,
"explanation": "Preprocessing enhances ICA performance by decorrelating signals and reducing noise."
},
{
"id": 59,
"questionText": "Scenario: ICA applied to financial data. Extracted component shows extreme values occasionally. Reason?",
"options": [
"Gaussian assumption violated",
"Algorithm error",
"Data missing",
"Represents independent market shocks or events"
],
"correctAnswerIndex": 3,
"explanation": "ICA isolates independent events; extreme values may correspond to real shocks in sources."
},
{
"id": 60,
"questionText": "Scenario: ICA applied to audio mixture, one component silent. Best approach?",
"options": [
"Check source contribution; low-energy components may appear silent",
"Increase iterations",
"Mix signals randomly",
"Discard ICA result"
],
"correctAnswerIndex": 0,
"explanation": "Low-contribution sources produce faint components; it is normal in ICA separation."
},
{
"id": 61,
"questionText": "ICA assumes that the number of sources is:",
"options": [
"Always greater than mixtures",
"Less than or equal to the number of observed mixtures",
"Irrelevant",
"Equal to one"
],
"correctAnswerIndex": 1,
"explanation": "ICA cannot separate more sources than observed signals; otherwise, the problem is underdetermined."
},
{
"id": 62,
"questionText": "Scenario: ICA applied on images of overlapping objects. Components are partially mixed. Solution?",
"options": [
"Randomly rotate components",
"Discard ICA",
"Improve preprocessing, reduce noise, adjust number of components",
"Increase output dimension"
],
"correctAnswerIndex": 2,
"explanation": "Better preprocessing and correct component selection improve separation quality."
},
{
"id": 63,
"questionText": "Scenario: ICA applied to audio, separated component inverted. Why?",
"options": [
"Algorithm failed",
"Gaussian assumption violated",
"Sign ambiguity is inherent in ICA",
"Noise dominates"
],
"correctAnswerIndex": 2,
"explanation": "ICA components may be scaled and inverted; this does not affect independence."
},
{
"id": 64,
"questionText": "Scenario: ICA applied to mixed audio signals, one component shows slight distortion. Likely cause?",
"options": [
"Sources are not perfectly independent or noise present",
"Algorithm failure",
"Gaussian assumption violated",
"Too few iterations"
],
"correctAnswerIndex": 0,
"explanation": "ICA assumes independence; slight dependence or noise can cause minor distortions in separated components."
},
{
"id": 65,
"questionText": "Scenario: You apply ICA to multi-sensor EEG recordings. Some components show mixed brain regions. Reason?",
"options": [
"Physical sensors capture overlapping signals; ICA cannot fully separate",
"Algorithm failed",
"Data is Gaussian",
"Number of components too high"
],
"correctAnswerIndex": 0,
"explanation": "ICA separates independent sources, but sensor overlap can mix contributions from multiple regions."
},
{
"id": 66,
"questionText": "Scenario: ICA applied to images; one extracted component is nearly zero. Likely cause?",
"options": [
"Algorithm error",
"The source has very low variance or contribution",
"Gaussian assumption violated",
"Too many iterations"
],
"correctAnswerIndex": 1,
"explanation": "Low-variance sources appear faint or nearly zero in ICA outputs; this is normal behavior."
},
{
"id": 67,
"questionText": "Scenario: ICA applied to EEG data, but noise dominates. Best approach?",
"options": [
"Ignore noise",
"Increase number of components",
"Preprocess signals to remove artifacts before ICA",
"Apply random scaling"
],
"correctAnswerIndex": 2,
"explanation": "Preprocessing to remove artifacts improves ICA performance and separation quality."
},
{
"id": 68,
"questionText": "Scenario: ICA applied on financial data. Extracted components show skewed distributions. Why?",
"options": [
"Algorithm failed",
"ICA extracts non-Gaussian independent components; skewness is expected",
"Gaussian assumption violated",
"Data incomplete"
],
"correctAnswerIndex": 1,
"explanation": "ICA maximizes non-Gaussianity, so skewed components reflect true independent sources."
},
{
"id": 69,
"questionText": "Scenario: ICA applied to mixed audio; one component appears inverted. Reason?",
"options": [
"Algorithm error",
"Noise dominates",
"ICA components are identifiable only up to sign (polarity) ambiguity",
"Gaussian assumption violated"
],
"correctAnswerIndex": 2,
"explanation": "Sign ambiguity is inherent in ICA; inverted components are valid independent sources."
},
{
"id": 70,
"questionText": "Scenario: ICA applied to multi-channel EEG, some channels missing. Effect?",
"options": [
"Algorithm produces random components",
"Cannot fully recover all independent sources",
"Gaussian assumption fails",
"ICA works normally"
],
"correctAnswerIndex": 1,
"explanation": "ICA requires enough observed signals; missing channels reduce information and prevent full source recovery."
},
{
"id": 71,
"questionText": "Scenario: You applied ICA on EEG data with 64 channels and extracted 64 components. Some components are mixtures of multiple brain signals. Likely reason?",
"options": [
"Algorithm failed",
"Gaussian assumption violated",
"Sources are not perfectly independent and sensors pick overlapping signals",
"Noise dominates"
],
"correctAnswerIndex": 2,
"explanation": "Even with sufficient components, overlapping signals and partial dependence can cause mixed components."
},
{
"id": 72,
"questionText": "Scenario: ICA applied to financial returns of multiple assets. Some components show extreme spikes. Interpretation?",
"options": [
"Algorithm failure",
"Gaussian sources assumption violated",
"Represents independent shocks in the market",
"Data insufficient"
],
"correctAnswerIndex": 2,
"explanation": "ICA isolates independent sources; extreme spikes can correspond to sudden market events or shocks."
},
{
"id": 73,
"questionText": "Scenario: You mix three audio sources into two channels. Applying ICA?",
"options": [
"Cannot fully recover all sources; problem is underdetermined",
"ICA works normally",
"Extra components are generated automatically",
"Components become Gaussian"
],
"correctAnswerIndex": 0,
"explanation": "ICA requires the number of observed mixtures ≥ number of sources; fewer mixtures make full recovery impossible."
},
{
"id": 74,
"questionText": "Scenario: ICA applied on multi-sensor EEG with strong noise. Some components dominated by noise. Recommended action?",
"options": [
"Ignore noise",
"Reduce number of components",
"Preprocess signals to reduce noise and artifacts before ICA",
"Apply PCA only"
],
"correctAnswerIndex": 2,
"explanation": "Noise can dominate ICA outputs; preprocessing ensures cleaner separation of meaningful sources."
},
{
"id": 75,
"questionText": "Scenario: ICA applied to images with overlapping handwritten letters. One extracted component is faint and noisy. Likely reason?",
"options": [
"Algorithm failed",
"Gaussian assumption violated",
"Low variance of source or high noise contribution",
"Too many components extracted"
],
"correctAnswerIndex": 2,
"explanation": "Low-contribution sources appear faint; preprocessing or filtering can improve component clarity."
},
{
"id": 76,
"questionText": "Scenario: ICA applied to mixed audio signals. Extracted components randomly scaled. Why?",
"options": [
"Gaussian assumption violated",
"Algorithm error",
"ICA components are identifiable up to scaling and permutation",
"Data insufficient"
],
"correctAnswerIndex": 2,
"explanation": "Scaling ambiguity is inherent in ICA; absolute amplitude cannot be determined."
},
{
"id": 77,
"questionText": "Scenario: ICA applied to multi-sensor EEG data; one component contains both eye-blink artifacts and brain signals. Best practice?",
"options": [
"Remove entire component",
"Apply PCA only",
"Ignore and keep all components",
"Carefully attenuate artifact without removing valuable brain signals"
],
"correctAnswerIndex": 3,
"explanation": "Overlapping components require careful processing to preserve meaningful information while reducing artifacts."
},
{
"id": 78,
"questionText": "Scenario: ICA applied on financial time series. One extracted component shows skewed returns distribution. Interpretation?",
"options": [
"Algorithm failed",
"Reflects independent non-Gaussian factors driving the market",
"Gaussian assumption violated",
"Data insufficient"
],
"correctAnswerIndex": 1,
"explanation": "ICA extracts non-Gaussian independent components; skewness indicates independent market factors."
},
{
"id": 79,
"questionText": "Scenario: ICA applied to audio signals. One extracted component is nearly silent. Reason?",
"options": [
"Algorithm failed",
"Source contribution to the mixture is very low",
"Gaussian assumption violated",
"Random initialization failed"
],
"correctAnswerIndex": 1,
"explanation": "Faint components reflect sources with low variance or weak presence in the mixtures."
},
{
"id": 80,
"questionText": "Scenario: ICA applied on EEG with missing channels. Effect?",
"options": [
"Algorithm works normally",
"Components are random",
"Cannot fully recover all independent sources",
"Gaussian assumption violated"
],
"correctAnswerIndex": 2,
"explanation": "ICA requires enough observed signals; missing channels reduce information and prevent full separation."
},
{
"id": 81,
"questionText": "Scenario: ICA applied to high-dimensional images. Some components are mixtures of multiple features. Recommended action?",
"options": [
"Increase number of components",
"Randomly remove features",
"Ignore and use ICA directly",
"Apply PCA for dimensionality reduction and whitening before ICA"
],
"correctAnswerIndex": 3,
"explanation": "Dimensionality reduction and whitening improve ICA stability and separation in high-dimensional data."
},
{
"id": 82,
"questionText": "Scenario: ICA applied on mixed audio, some frequency bands overlap. Outcome?",
"options": [
"Complete separation",
"Algorithm fails entirely",
"No effect",
"Partial separation; overlapping frequencies reduce effectiveness"
],
"correctAnswerIndex": 3,
"explanation": "ICA assumes independence; overlapping frequency content may limit perfect separation."
},
{
"id": 83,
"questionText": "Scenario: ICA applied on EEG signals with eye-blink artifacts. Some components contain both artifacts and brain signals. Action?",
"options": [
"Keep all components",
"Apply PCA only",
"Remove entire component",
"Selective attenuation to remove artifacts without losing brain activity"
],
"correctAnswerIndex": 3,
"explanation": "Careful component processing preserves useful information while reducing artifacts."
},
{
"id": 84,
"questionText": "Scenario: ICA applied to two mixed images; extracted components inverted in polarity. Reason?",
"options": [
"Gaussian assumption violated",
"Sign ambiguity is inherent in ICA",
"Noise dominates",
"Algorithm error"
],
"correctAnswerIndex": 1,
"explanation": "ICA components can have arbitrary sign; inversion does not affect independence."
},
{
"id": 85,
"questionText": "Scenario: ICA applied to multi-sensor EEG with strong artifacts. Components show partial mixing. Likely reason?",
"options": [
"Data insufficient",
"Gaussian assumption violated",
"Algorithm failed",
"Sources are not perfectly independent or sensor overlap exists"
],
"correctAnswerIndex": 3,
"explanation": "Partial dependence or overlapping sensor recordings can cause mixed components."
},
{
"id": 86,
"questionText": "Scenario: ICA applied to audio mixture; faint component extracted. Best interpretation?",
"options": [
"Algorithm failed",
"Component corresponds to source with low contribution to mixture",
"Gaussian assumption violated",
"Too many iterations"
],
"correctAnswerIndex": 1,
"explanation": "Low-energy sources produce faint components, which is normal in ICA."
},
{
"id": 87,
"questionText": "Scenario: ICA applied on EEG with 128 channels. Extracted components appear noisy. Recommended step?",
"options": [
"Discard ICA result",
"Randomly mix channels",
"Increase number of components",
"Preprocess with filtering and artifact removal before ICA"
],
"correctAnswerIndex": 3,
"explanation": "Preprocessing improves signal quality and ICA separation."
},
{
"id": 88,
"questionText": "Scenario: ICA applied to financial signals. Extracted component has extreme outliers. Likely interpretation?",
"options": [
"Algorithm failure",
"Gaussian assumption violated",
"Represents independent extreme market events",
"Data missing"
],
"correctAnswerIndex": 2,
"explanation": "ICA isolates independent sources; extreme values may correspond to real shocks or events."
},
{
"id": 89,
"questionText": "Scenario: ICA applied on multi-sensor EEG; some components appear to be mixtures of several brain sources. Why?",
"options": [
"Algorithm failed",
"Partial dependence or overlapping sensor recordings",
"Gaussian assumption violated",
"Number of components too high"
],
"correctAnswerIndex": 1,
"explanation": "ICA assumes independence; overlapping measurements can create mixed components."
},
{
"id": 90,
"questionText": "Scenario: ICA applied to mixed audio; one component appears silent. Action?",
"options": [
"Randomly re-initialize algorithm",
"Discard ICA result",
"Increase number of components",
"Check source contribution; low-energy sources may appear silent"
],
"correctAnswerIndex": 3,
"explanation": "Silent components usually reflect sources with minimal contribution; not a failure."
},
{
"id": 91,
"questionText": "Scenario: ICA applied on EEG with missing channels. Solution?",
"options": [
"Collect more channels or use methods for missing data",
"Ignore missing channels",
"Randomly fill missing data",
"Apply PCA only"
],
"correctAnswerIndex": 0,
"explanation": "ICA requires sufficient observed signals; missing channels prevent full source recovery."
},
{
"id": 92,
"questionText": "Scenario: ICA applied on mixed images; low-variance components faint. Best approach?",
"options": [
"Discard faint components",
"Enhance preprocessing or use more observations",
"Increase ICA iterations only",
"Randomly mix images"
],
"correctAnswerIndex": 1,
"explanation": "Low-variance sources require preprocessing and sufficient data for effective separation."
},
{
"id": 93,
"questionText": "Scenario: ICA applied to EEG with eye-blink and muscle artifacts. Some components overlap. Action?",
"options": [
"Apply PCA only",
"Keep all components",
"Selective attenuation to remove artifacts without losing brain signal",
"Remove all overlapping components"
],
"correctAnswerIndex": 2,
"explanation": "Overlapping components require careful attenuation to preserve meaningful signals."
},
{
"id": 94,
"questionText": "Scenario: ICA applied on audio mixture; overlapping frequency content. Effect?",
"options": [
"Algorithm fails",
"Partial separation; overlapping reduces effectiveness",
"No effect",
"Complete separation"
],
"correctAnswerIndex": 1,
"explanation": "ICA cannot perfectly separate overlapping frequencies; independence assumption is partially violated."
},
{
"id": 95,
"questionText": "Scenario: ICA applied to EEG with noisy channels. Recommended preprocessing?",
"options": [
"Filtering, artifact removal, centering, whitening",
"Randomly remove channels",
"Apply ICA directly",
"Ignore preprocessing"
],
"correctAnswerIndex": 0,
"explanation": "Preprocessing improves signal quality and ICA separation."
},
{
"id": 96,
"questionText": "Scenario: ICA applied to financial data; extracted component shows heavy skew. Interpretation?",
"options": [
"Data missing",
"Represents independent non-Gaussian market factor",
"Algorithm failed",
"Gaussian assumption violated"
],
"correctAnswerIndex": 1,
"explanation": "ICA identifies independent non-Gaussian factors; skewness reflects this property."
},
{
"id": 97,
"questionText": "Scenario: ICA applied to multi-channel EEG; some components inverted. Reason?",
"options": [
"Algorithm error",
"Noise dominates",
"Sign ambiguity inherent in ICA",
"Gaussian assumption violated"
],
"correctAnswerIndex": 2,
"explanation": "ICA components may appear inverted due to sign ambiguity; still valid."
},
{
"id": 98,
"questionText": "Scenario: ICA applied to audio signals; faint or near-zero components. Best explanation?",
"options": [
"Algorithm failed",
"Gaussian assumption violated",
"Low-contribution sources produce faint components",
"Random initialization failed"
],
"correctAnswerIndex": 2,
"explanation": "Components magnitude reflects source contribution; faint components indicate weak sources."
},
{
"id": 99,
"questionText": "Scenario: ICA applied on EEG; extracted components partially mixed. Solution?",
"options": [
"Increase ICA iterations only",
"Randomly mix channels",
"Improve preprocessing, adjust number of components, and check sensor overlap",
"Discard ICA result"
],
"correctAnswerIndex": 2,
"explanation": "Proper preprocessing and component selection improve separation of partially mixed sources."
},
{
"id": 100,
"questionText": "Scenario: ICA applied on audio mixture; one component dominated by noise. Best practice?",
"options": [
"Increase number of components",
"Apply PCA only",
"Preprocess to remove noise before ICA",
"Ignore noise"
],
"correctAnswerIndex": 2,
"explanation": "Noise can dominate ICA; preprocessing ensures meaningful source separation."
}
]
}
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