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.
| Package | Class | Description |
|---|---|---|
| preprocessing | FastICA | Requires the FastICA library |
| aggregation | Vlad | Requires the vlfeat library |
| aggregation | Fisher | Requires the vlfeat library |
| featselection | FEAST | Requires the FEAST library (download link is next to "Archive" somewhere in the middle of the page) and MIToolbox (included in the FEAST zip file) |
| classification | L1MCCA | Requires the [tensor] (http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html) toolbox |
| classification | LIBSVMFast | Requires the libsvm library |
| classification | MLTboxMulticlass | Requires Matlab version r2015a or newer |
| classification | MLDA | Requires Matlab version r2014 or newer |
| util | LSLWrapper | Requires the Labstreaminglayer library |
Some examples are available that are based on the datasets that can be found below.
- exampleDefault, performs a simple experiment on Dataset I & II
- exampleOptimal, performs an experiment with the optimal settings for Dataset I & II
- exampleEpoc, performs an experiment for the dataset that was recorded with an EPOC device (Dataset III)
- exampleLSL, an example on how to perform online classification of EEG signals with the help of the Labstreaminglayer interface for Matlab.
| Title | Description | Download Link |
|---|---|---|
| EEG SSVEP Dataset I | EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) presented in isolation have been used for the visual stimulation. The EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. | Dataset I |
| EEG SSVEP Dataset II | EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) presented simultaneously have been used for the visual stimulation. The EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. | Dataset II |
| EEG SSVEP Dataset III | EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) presented simultaneously have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals. | Dataset III |
[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