Neural Bag-of-Features for EGG classification

Using Bag-of-Features (BoF) to classify EEG time-series data

This repository demonstrates how to use the Neural BoF model to classify time-series data. In contrast to other well-known models tailored for time-series classification, the BoF model discards most of the spatial information contained in the time-series. This can be especially advantageous when we want to detect certain features in a time-series (e.g., EEG, ECG, etc).

The supplied code evaluates the following models:

Model	Accuracy
MLP	73.2 %
GRU	76.2 %
BoF	67.3 %
Neural BoF	86.7 %

If you use this code in your work please cite the following paper:

@inproceedings{neural-bof-eeg,
        title       = "Time-series Classification Using Neural Bag-of-Features",
	author      = "Passalis, Nikolaos and Tsantekidis, Avraam and Tefas, Anastasios and Kanniainen, Juho and Gabbouj, Moncef and Iosifidis, Alexandros",
	booktitle   = "Proceedings of the 25th European Signal Processing Conference",
	pages       = "TBA",
	year        = "2017"
}

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