moabb.datasets.Stieger2021#

class moabb.datasets.Stieger2021(interval=[0, 3], sessions=None)[source]#

Motor Imagery dataset from Stieger et al. 2021.

Dataset summary

#Subj

#Chan

#Classes

#Trials

Trial length

Freq

#Session

#Runs

Total_trials

62

64

4

450

3s

1000Hz

7 or 11

1

250000

The main goals of our original study were to characterize how individuals learn to control SMR-BCIs and to test whether this learning can be improved through behavioral interventions such as mindfulness training. Participants were initially assessed for baseline BCI proficiency and then randomly assigned to an 8-week mindfulness intervention (Mindfulness-based stress reduction), or waitlist control condition where participants waited for the same duration as the MBSR class before starting BCI training, but were offered a comparable MBSR course after completing all experimental requirements. Following the 8-weeks, participants returned to the lab for 6 to 10 sessions of BCI training.

All experiments were approved by the institutional review boards of the University of Minnesota and Carnegie Mellon University. Informed consents were obtained from all subjects. In total, 144 participants were enrolled in the study and 76 participants completed all experimental requirements. Seventy-two participants were assigned to each intervention by block randomization, with 42 participants completing all sessions in the experimental group (MBSR before BCI training; MBSR subjects) and 34 completing experimentation in the control group. Four subjects were excluded from the analysis due to non-compliance with the task demands and one was excluded due to experimenter error. We were primarily interested in how individuals learn to control BCIs, therefore analysis focused on those that did not demonstrate ceiling performance in the baseline BCI assessment (accuracy above 90% in 1D control). The dataset descriptor presented here describes data collected from 62 participants: 33 MBSR participants (Age=42+/-15, (F)emale=26) and 29 controls (Age=36+/-13, F=23). In the United States, women are twice as likely to practice meditation compared to men. Therefore, the gender imbalance in our study may result from a greater likelihood of women to respond to flyers offering a meditation class in exchange for participating in our study.

For all BCI sessions, participants were seated comfortably in a chair and faced a computer monitor that was placed approximately 65cm in front of them. After the EEG capping procedure (see data acquisition), the BCI tasks began. Before each task, participants received the appropriate instructions. During the BCI tasks, users attempted to steer a virtual cursor from the center of the screen out to one of four targets. Participants initially received the following instructions: “Imagine your left (right) hand opening and closing to move the cursor left (right). Imagine both hands opening and closing to move the cursor up. Finally, to move the cursor down, voluntarily rest; in other words, clear your mind.” In separate blocks of trials, participants directed the cursor toward a target that required left/right (LR) movement only, up/down (UD) only, and combined 2D movement (2D)30. Each experimental block (LR, UD, 2D) consisted of 3 runs, where each run was composed of 25 trials. After the first three blocks, participants were given a short break (5-10 minutes) that required rating comics by preference. The break task was chosen to standardize subject experience over the break interval. Following the break, participants competed the same 3 blocks as before. In total, each session consisted of 2 blocks of each task (6 runs total of LR, UD, and 2D control), which culminated in 450 trials performed each day.

Online BCI control of the cursor proceeded in a series of steps. The first step, feature extraction, consisted of spatial filtering and spectrum estimation. During spatial filtering, the average signal of the 4 electrodes surrounding the hand knob of the motor cortex was subtracted from electrodes C3 and C4 to reduce the spatial noise. Following spatial filtering, the power spectrum was estimated by fitting an autoregressive model of order 16 to the most recent 160 ms of data using the maximum entropy method. The goal of this method is to find the coefficients of a linear all-pole filter that, when applied to white noise, reproduces the data’s spectrum. The main advantage of this method is that it produces high frequency resolution estimates for short segments of data. The parameters are found by minimizing (through least squares) the forward and backward prediction errors on the input data subject to the constraint that the filter used for estimation shares the same autocorrelation sequence as the input data. Thus, the estimated power spectrum directly corresponds to this filter’s transfer function divided by the signal’s total power. Numerical integration was then used to find the power within a 3 Hz bin centered within the alpha rhythm (12 Hz). The translation algorithm, the next step in the pipeline, then translated the user’s alpha power into cursor movement. Horizontal motion was controlled by lateralized alpha power (C4 - C3) and vertical motion was controlled by up and down regulating total alpha power (C4 + C3). These control signals were normalized to zero mean and unit variance across time by subtracting the signals’ mean and dividing by its standard deviation. A balanced estimate of the mean and standard deviation of the horizontal and vertical control signals was calcu- lated by estimating these values across time from data derived from 30 s buffers of individual trial type (e.g., the normalized control signal should be positive for right trials and negative for left trials, but the average of left and right trials should be zero). Finally, the normalized control signals were used to update the position of the cursor every 40 ms.

References

1

Stieger, J. R., Engel, S. A., & He, B. (2021). Continuous sensorimotor rhythm based brain computer interface learning in a large population. Scientific Data, 8(1), 98. https://doi.org/10.1038/s41597-021-00883-1

Notes

New in version 1.1.0.

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