WESN-emulated motor execution EEG data

<p>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&nbsp; [2].</p> <p>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.</p> <p>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 <em>channel_labels.json</em> 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&nbsp;<em>nodes.json</em> file indicates the specific pair of channels composing each of these nodes. These&nbsp;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.</p> <p>[1] Strypsteen, Thomas, and Alexander Bertrand. "A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks."&nbsp;<em>arXiv preprint arXiv:2308.08379</em> (2023).</p> <p>[2] Schirrmeister, Robin Tibor, et al. "Deep learning with convolutional neural networks for EEG decoding and visualization." <em>Human brain mapping</em> 38.11 (2017): 5391-5420.</p>