Classes
interface	IPredictor< T >
	Interface for all predictors. More...

interface	IPredictorPipeable
	Extends the typical pipeable to include training controls SharpBCI uses these functions to interface with the prediction mechanisms. More...

class	AggregatePredictionPipeable
	A pipeable which aggregates received EEGEvents into an array based on the reported EEGEvent timestamp and then uses an IPredictor<EEGEvent[]> to train/classify on them. More...

class	Predictor
	An IPredictor which uses a 3-dimensional loci of points in the form of an array of EEGEvent's to classify EEG data Uses nearest neighbor predictions with distance computed by the abstract Compute(double[] x, double[] y) function Predictions occur by computing the distance between the incoming sample and all training samples, pulls the k nearest neighbors, and determines the final prediction. More...

class	AggregateKNNCorrelationPredictor

class	AggregateKNNDTWPredictor

class	TournamentArtifactPipeable
	An artifact detector which uses a TournamentArtifactDectector on a per-channel basis to detect artifacts Input types: EEG, Output types: EEG. More...

interface	IArtifactDetector
	An interface which defines an abstract artifact detector You can implement this to utilize classes which find the best detector implementation. More...

class	ARArtifactDetector
	A detector which uses an underlying AR model to detect artifacts. More...

class	TournamentArtifactDetector
	The TournamentArtifactDetector holds a "tournament" of some number ARArtifactDetectors (termed competitors) The majority consensus of the most well-fit models is the final determination of whether or not the data is an artifact. More...

class	AsyncStreamReader

class	AsyncStreamWriter
	Used internally to asynchronously write a string to a file stream. More...

class	EEGDeviceAdapter
	An abstract base class from which all device adapters should inherit. More...

class	RemoteOSCAdapter
	An EEGDeviceAdapter which connects to an EEGDevice via OSC It then asynchronously listens for messages on a given OSC socket and emits the appropriate EEGEvents for the received OSC message Target Hardware: Muse 2014 and Muse 2016 (via android-bridge.apk) More...

struct	DummyAdapterSignal
	A simple struct which defines a signal of given frequencies and amplitudes (per-channel) More...

class	InstrumentedDummyAdapter
	An EEGDeviceAdapter which is used to emit a certain set of signals via StartSignal. More...

class	CSVReadAdapter
	A device adapter that can be used to replay or reemit the data logged during a previous session. More...

class	DummyAdapter
	A version of InstrumentedDummyAdapter which constantly emits a given DummyAdapterSignal Generally used for smoke-testing / debugging your SharpBCI pipeline. More...

class	DynamicTimeWarping

class	SeriesVariable

interface	IPreprocessor

class	Preprocessors

class	StandardizationPreprocessor

class	CentralizationPreprocessor

class	EEGEvent
	A class which represent various types of events relating to EEG data. More...

interface	IVectorizedSmoother< T >
	A common interface for a generic smoother which operates on arrays of type T Currently, only used by FFTPipeable for smoothing. More...

class	ExponentialVectorizedSmoother
	Implements a vectorized version of exponential smoothing Each value in the input array is assumed to belong to the same time series as the previous value at the same index in the last input array. More...

class	XCorrVectorizedSmoother

class	FFTPipeable
	A Pipeable which performs an FFT on each channel It outputs an FFTEvent every windowSize samples. More...

class	BandpassPipeable
	A pipeable which implements a band-pass filter on all incoming data Input types: EEGEvent Output types: EEGEvent. More...

class	SimpleFilterPipeable
	This pipeable is deprecated and may be removed at any point without warning. More...

class	FilterUtilities

interface	IFilter< T >

class	MultiFilter< T >

class	MovingAverageFilter

class	ExponentialFilter

class	RecursiveFilter

class	NarrowPassFilter

class	NotchFilter

class	ConvolvingFilter

class	ConvolvingSingleEndedFilter

class	ConvolvingDoubleEndedFilter

class	IndexableQueue< T >
	Weiss implementation of Circular Buffer with addition of indexability. More...

interface	ILogOutput

class	ConsoleLogger

class	FileLogger

class	Logger

class	MatrixUtils

interface	IPipeable
	All components of the SharpBCI pipeline are expected to implement this interface. More...

class	Pipeable

class	SerializedPipeline
	A class which represents the entire pipeline. More...

class	SerializedStage
	A class which represents a single stage in the pipeline Can be serialized into a JSON file. More...

class	SerializedConnectionInfo
	A class which represents a connection graph for the pipeline. More...

class	PipelineSerializer
	A static class to read SerializedStage files from a file.

class	SharpBCIConfig
	An object which configures a SharpBCI object. More...

class	SharpBCIBuilder
	A builder class for SharpBCIConfig. More...

class	TrainedEvent
	A generic event which indicates previously trained event occured. More...

class	SharpBCI
	This is the "main" class which you should create. More...

class	TrainedEventEmitter
	An end-point consumer which emits TrainedEvents it received Must only be connected to Pipeables which output TrainedEvents. More...

class	RawEventEmitter
	An end-point consumer which emits TrainedEvents it received Must only be connected to Pipeables which output EEGEvents. More...

class	EEGDeviceProducer
	Wraps an EEGDeviceAdapter with a Pipeable and emits EEGDataType.EEG events Should only be used as a producer not consumer. More...

class	StatsUtils
	A class for statistical utility functions.

class	OnlineVariance
	Simultaneously calculates sample mean and variance in O(1) using Welford-Knuth online algorithm. More...

class	ARModel
	A simple AR model class which can predict the next value of X given AR parameters (phi), a constant factor (i.e., E[noise(X)]), and previous values of X O(p) performance. More...

Enumerations
enum	DistanceMeasure { DistanceMeasure.Manhattan = 1, DistanceMeasure.Euclidean = 2, DistanceMeasure.SquaredEuclidean = 3, DistanceMeasure.Maximum = 4 }

enum	EEGDataType { EEGDataType.EEG = 0, EEGDataType.FFT_RAW = 1, EEGDataType.FFT_SMOOTHED = 2, EEGDataType.ALPHA_ABSOLUTE = 3, EEGDataType.BETA_ABSOLUTE = 4, EEGDataType.GAMMA_ABSOLUTE = 5, EEGDataType.DELTA_ABSOLUTE = 6, EEGDataType.THETA_ABSOLUTE = 7, EEGDataType.ALPHA_RELATIVE = 8, EEGDataType.BETA_RELATIVE = 9, EEGDataType.GAMMA_RELATIVE = 10, EEGDataType.DELTA_RELATIVE = 11, EEGDataType.THETA_RELATIVE = 12, EEGDataType.CONTACT_QUALITY = 13 }
	What type of EEG data this EEGEvent represents. More...

enum	LogLevel { LogLevel.INFO, LogLevel.WARNING, LogLevel.ERROR }
	Internal enum to indicate log level INFO = normal running information WARNING = possible error, but not fatal ERROR = fatal error occurred. More...

Enumeration Type Documentation

enum SharpBCI.DistanceMeasure

Enumerator
Manhattan
Euclidean
SquaredEuclidean
Maximum

enum SharpBCI.EEGDataType

What type of EEG data this EEGEvent represents.

IMPORTANT note to devs: not assigning a value to each field in this enum can break scenes that rely on it.

Enumerator
EEG	This represents microvolt data organized by channel. It may or may not be filtered for artifacts depending on pipeline layout. The length of EEGEvent data should equal the number of channels as reported by the underlying EEGDeviceAdapter.
FFT_RAW	Data resulting from a Fourier Transform on some number of samples of type = EEG events This EEGEvent has not been smoothed in any way, and is inherently more variable than FFT_SMOOTHED. Length of data array depends on window size of the Fourier Transform. Extra data is an int indicating what channel this event belongs to.
FFT_SMOOTHED	Data resulting from a Fourier Transform on some number of samples of type = EEG events This EEGEvent has been smoothed by some function which depends on the underlying pipeline. Length of data array depends on window size of the Fourier Transform. Extra data is an int indicating what channel this event belongs to.
ALPHA_ABSOLUTE	The sum of power in the alpha band (specifically about 7.5 Hz ~ 13 Hz) in decibels Length of data array is equal to the number of channels as reported by the underlying EEGDeviceAdapter.
BETA_ABSOLUTE	The sum of power in the beta band (specifically about 13 Hz ~ 30 Hz) in decibels Length of data array is equal to the number of channels as reported by the underlying EEGDeviceAdapter.
GAMMA_ABSOLUTE	The sum of power in the alpha band (specifically about 30 Hz ~ 44 Hz) in decibels Length of data array is equal to the number of channels as reported by the underlying EEGDeviceAdapter.
DELTA_ABSOLUTE	The sum of power in the alpha band (specifically about 1 Hz ~ 4 Hz) in decibels Length of data array is equal to the number of channels as reported by the underlying EEGDeviceAdapter.
THETA_ABSOLUTE	The sum of power in the alpha band (specifically about 4 Hz ~ 8 Hz) in decibels Length of data array is equal to the number of channels as reported by the underlying EEGDeviceAdapter.
ALPHA_RELATIVE	The power of the alpha band in relation to other bands. Length of data array should be equal to the underlying EEGDeviceAdapter.
BETA_RELATIVE	The power of the beta band in relation to other bands. Length of data array should be equal to the underlying EEGDeviceAdapter.
GAMMA_RELATIVE	The power of the gamma band in relation to other bands. Length of data array should be equal to the underlying EEGDeviceAdapter.
DELTA_RELATIVE	The power of the delta band in relation to other bands. Length of data array should be equal to the underlying EEGDeviceAdapter.
THETA_RELATIVE	The power of the theta band in relation to other bands. Length of data array should be equal to the underlying EEGDeviceAdapter.
CONTACT_QUALITY	Indicates a change in contact quality for EEG channels Length of data array should be equal to the underlying EEGDeviceAdapter. The data array indicates a qualitative metric of current connectivity on a per channel basis. Current semantics: 4 = no contact, 2 = poor contact, 1 = good contact

enum SharpBCI.LogLevel

Internal enum to indicate log level INFO = normal running information WARNING = possible error, but not fatal ERROR = fatal error occurred.

Enumerator
INFO
WARNING
ERROR

Classes

Enumerations

Enumeration Type Documentation