moabb.datasets.EPFLP300

class moabb.datasets.EPFLP300[source][source]

P300 dataset from Hoffmann et al 2008.

Dataset from the paper [1].

Dataset Description

In the present work a six-choice P300 paradigm is tested using a population of five disabled and four able-bodied subjects. Six different images were flashed in random order with a stimulus interval of 400 ms. Users were facing a laptop screen on which six im- ages were displayed. The images showed a television, a telephone, a lamp, a door, a window, and a radio.

The images were flashed in random sequences, one image at a time. Each flash of an image lasted for 100 ms and during the following 300 ms none of the images was flashed, i.e. the interstimulus interval was 400 ms. The EEG was recorded at 2048 Hz sampling rate from 32 electrodes placed at the standard positions of the 10-20 international system. The system was tested with five disabled and four healthy subjects. The disabled subjects were all wheelchair-bound but had varying communication and limb muscle control abilities (Subjects 1 to 5). In particular, Subject 5 was only able to perform extremely slow and relatively uncontrolled movements with hands and arms. Due to a severe hypophony and large fluctuations in the level of alertness, communication with subject 5 was very difficult, which is why its data is not available in this dataset. Subjects 6 to 9 were PhD students recruited from our laboratory (all male, age 30 ± 2.3).

Each subject completed four recording sessions. The first two sessions were performed on one day and the last two sessions on another day. For all subjects the time between the first and the last session was less than two weeks. Each of the sessions consisted of six runs, one run for each of the six images. The duration of one run was approximately one minute and the duration of one session including setup of electrodes and short breaks between runs was approximately 30 minutes. One session comprised on average 810 trials, and the whole data for one subject consisted on average of 3240 trials.

References

1

Hoffmann, U., Vesin, J-M., Ebrahimi, T., Diserens, K., 2008. An efficient P300-based brain-computer interfacefor disabled subjects. Journal of Neuroscience Methods . https://doi.org/10.1016/j.jneumeth.2007.03.005

Methods

data_path(subject[, path, force_update, …])

Get path to local copy of a subject data.

download([subject_list, path, force_update, …])

Download all data from the dataset.

get_data([subjects])

Return the data correspoonding to a list of subjects.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source][source]

Get path to local copy of a subject data.

Parameters
  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None **Deprecated**) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type

list of str