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(Somewhat) Validated Experiments

All experiments from muse-lsl's notebooks

Visual P300 with Oddball paradigm

The visual P300 is a spike that occurs 300ms after perceiving a visual stimulus that has implications on decision making. This was validated in Muse by AB with the Oddball paradigm, in which low-probability target items (oddballs) are interspersed with high probability non-target items. With AB's paradigm, the experiment takes about 10 minutes to run (5 x 2 minute trials). The best pipeline for classifying P300s after collecting a dataset (for use in BCI) was found to be .77 AUC with ERP Covariance + MDM (Riemannian Geometry based). This accuracy is apparently good but not outstanding as far as BCIs go.

Unfortunately, I've also heard from Hubert that, in testing 10 different people, some of them weren't able to get very good ERPs. This could be due to their neuroanatomy, as EEG expresses pretty differently between peopl

	#!/usr/bin/python2


	# Copyright (C) 2016 Sixten Bergman
	# License WTFPL
	#
	# This program is free software. It comes without any warranty, to the extent
	# permitted by applicable law.
	# You can redistribute it and/or modify it under the terms of the Do What The
	# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

	function [maxtab, mintab]=peakdet(v, delta, x)
	%PEAKDET Detect peaks in a vector
	% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
	% maxima and minima ("peaks") in the vector V.
	% MAXTAB and MINTAB consists of two columns. Column 1
	% contains indices in V, and column 2 the found values.
	%
	% With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
	% in MAXTAB and MINTAB are replaced with the corresponding
	% X-values.