WESN-emulated motor execution EEG data

This dataset contains EEG measured during a motor execution task and processed as to emulate EEG originating from a wireless EEG sensor network composed of mini-EEG devices, as presented in [1]. It is a processed version of the original High Gamma dataset of  [2].

In mini-EEG devices, we cannot measure the potential between a given electrode and a distant reference (e.g. the mastoid or Cz electrode) , as we would in traditional EEG caps. Instead, we can only record the local potential between two nearby electrodes belonging to the same sensor device. To emulate this setting using a standard cap-EEG recording, we we can considers= each pair of electrodes within a certain maximum distance as a candidate electrode pair or node. By subtracting one channel from the other, we remove the common far-distance reference and obtain a signal that emulates the local potential of the node.

We applied this method to the High Gamma dataset as follows. First, the 44 channels covering the motor cortex were selected. These channels are indicated in the channel_labels.json file. Then, the rereferencing between channels with a distance threshold of 3 cm was applied, yielding a set of 286 candidate electrode pairs or nodes. The nodes.json file indicates the specific pair of channels composing each of these nodes. These have an average inter-electrode distance of 1.98 cm and a standard deviation of 0.59 cm. Finally, we applied the preprocessing described in [2], i.e., resampling at 250 Hz, highpass filtering above 4 Hz, standardizing the per-node mean and variance to 0 and 1 respectively, and extracting a window of 4.5 seconds for each trial.

[1] Strypsteen, Thomas, and Alexander Bertrand. "A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks." arXiv preprint arXiv:2308.08379 (2023).

[2] Schirrmeister, Robin Tibor, et al. "Deep learning with convolutional neural networks for EEG decoding and visualization." Human brain mapping 38.11 (2017): 5391-5420.