SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features

Algorithm 1

Specify the appropriate threshold value.

	Input:  PhaseSig,         ▷ instantaneous phase signal
	BestBreakpointsAmount,          ▷appropriate percentage of breakpoints
	Errormin, Errormax,     ▷minimum and maximum error values tolerated between lines and the signal
	EEG_Length          ▷number of samples in the input EEG signal
	Output: BestThreshold        ▷ appropriate threshold value
	If BestBreakpointsAmount ≥ 100 then:
	BestThreshold⟵ Errormin
	return BestThreshold
	Else
	(VectErrors, NbBreakpoints, …)  ⟵  PLS(PhaseSig, Errormin)
	▷PLS(signal, threshold): segment the signal using PLR with respect to the
	threshold value
	▷VectErrors: vector of error values sorted in increasing order
	▷NbBreakpoints: number of breakpoints
	(Errormax closest value to Errormax from VectErrors
	((VectErrors, NbBreakpoints, …)  ⟵  PLS(PhaseSig, Errormax)
	(
	(If BestBreakpointsAmount ≤ ActualBreakpointsAmount then:
	(BestThreshold ⟵  Errormax
	return BestThreshold
	Else
	While BestThreshold has not been assigned a value do:
	(VectErrors, NbBreakpoints, …)  ⟵  PLS(PhaseSig, StepError)
	Recalculate ActualBreakpointsAmount
	If BestBreakpointsAmount > ActualBreakpointsAmount then:
	While BestThreshold has not been assigned a value do:
	(VectErrors, NbBreakpoints, …)  ⟵  PLS(PhaseSig, ThStep)
	Recalculate ActualBreakpointsAmount
	If BestBreakpointsAmount > ActualBreakpointsAmount then:
	BestThreshold ⟵  threshold that gives the closet ActualBreakpointsAmount to BestBreakpointsAmount
	return BestThreshold
	End if
	Recalculate THStep
	End while
	End if
	End while
	End if
	End if