This software is released as part of the EU-funded research project MAMEM for supporting experimentation in EEG signals. It follows a modular architecture that allows the fast execution of experiments of different configurations with minimal adjustments of the code. The experimental pipeline consists of the Experimenter class which acts as a wrapper of five more underlying parts;

• The Session object: Used for loading the dataset and segmenting the signal according to the periods that the SSVEP stimuli were presented during the experiment. The signal parts are also annotated with a label according to the stimulus frequency.
• The Preprocessing object: Includes methods for modifying the raw EEG signal.
• The Feature Extraction object: Performs feature extraction algorithms for extracting numerical features from the EEG signals.
• The Feature Selection object: Selects the most important features that were extracted in the previous step.
• The Classification object: Trains a classification model for predicting the label of unknown samples.

The usage of some classes of the framework is limited by the following requirements.

Some examples are available that are based on the datasets that can be found below.

• exampleCSP, extract common spatial patterns in dataset III of [BCI competition II] (http://www.bbci.de/competition/ii/)
• exampleCombiCCA, SSVEP recognition using the CombinedCCA method from [2]. Based on this [dataset] (ftp://sccn.ucsd.edu/pub/cca_ssvep)
• exampleDefault, performs a simple experiment on Dataset I & II
• exampleEPOCCCASVM, SSVEP recognition using SVM on the CCA coefficients, based on Dataset III
• exampleERRP, recognition of error related potentials, based on the [dataset] (https://github.com/flowersteam/self_calibration_BCI_plosOne_2015) provided by [3]
• exampleEarlyFusion, demonstrates how to merge features extracted by different electrode channels, based on Dataset II.
• exampleEpoc, performs an experiment for the dataset that was recorded with an EPOC device (Dataset III)
• exampleITCCA, SSVEP recognition using the ITCCA method from [2]. Based on this [dataset] (ftp://sccn.ucsd.edu/pub/cca_ssvep)
• exampleL1MCCA, SSVEP recognition using the L1MCCA method from [2]. Based on this [dataset] (ftp://sccn.ucsd.edu/pub/cca_ssvep)
• exampleLSL, Online recognition of SSVEP signals using the [LSL library] (https://github.com/sccn/labstreaminglayer).
• exampleLateFusion, merging the output of different classifiers by majority voting, based on Dataset II.
• exampleMotorPWelch, classification of right/left hand motor imagery based on the dataset III of [BCI competition II] (http://www.bbci.de/competition/ii/)
• exampleOptimal, performs an experiment with the optimal settings for Dataset I & II
• exampleSMFA, SSVEP recognition with using SMFA [4]

[1] Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos and Ioannis Kompatsiaris, "Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs", Technical Report - eprint arXiv:1602.00904, February 2016

[2] M. Nakanishi, Y. Wang, Y.T. Wang, and T.P. Jung, “A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials,” PLoS ONE, p. e0140703, October 2015.

[3] Iturrate, Iñaki, Jonathan Grizou, Jason Omedes, Pierre-Yves Oudeyer, Manuel Lopes, and Luis Montesano. "Exploiting task constraints for self-calibrated brain-machine interface control using error-related potentials." PloS one 10, no. 7 (2015): e0131491. Harvard

[4] Maronidis, Anastasios, Anastasios Tefas, and Ioannis Pitas. "Subclass Marginal Fisher Analysis." In Computational Intelligence, 2015 IEEE Symposium Series on, pp. 1391-1398. IEEE, 2015.