---
license: pddl
tags:
- eeg
- medical
- clinical
- classification
- schizophrenia
- simon task
---
# Albrecht2019: Schizophrenia Classification Dataset with Modified Simon Task
The Albrecht2019 dataset originates from a study investigating reinforcement learning under cognitive conflict in individuals with schizophrenia (PSZ) and healthy controls. The dataset includes both behavioral and EEG recordings collected during a modified Simon task, which introduces response conflict as an implicit cognitive cost during reinforcement learning.

A total of 78 participants took part in the study: 46 individuals with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, and 32 healthy controls. EEG data were recorded using a 64-channel Brain Vision system at a 1000 Hz sampling rate. Data were preprocessed and artifact-corrected using the EEGLAB pipeline, and ICA was applied for eye-blink removal. EEG epochs were extracted around stimulus and feedback events to capture conflict-evoked and prediction-error-related activity, particularly in the theta band (4–7 Hz).

Participants completed a reinforcement learning version of the Simon task. On each trial, a stimulus was associated with probabilistic reward or punishment outcomes, modulated by whether the trial involved a congruent or conflict-inducing response. This design enabled the dissociation of positive and negative prediction error (PE) learning biases under cognitive effort. A subsequent transfer phase assessed stimulus preferences to infer learning outcomes.
## Paper
Albrecht, M. A., Waltz, J. A., Cavanagh, J. F., Frank, M. J., & Gold, J. M. (2019). **Increased conflict-induced slowing, but no differences in conflict-induced positive or negative prediction error learning in patients with schizophrenia**. _Neuropsychologia_, 123, 131-140.

DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's ([EEG-Bench](https://github.com/ETH-DISCO/EEG-Bench)) and other's work on EEG benchmarking.

## Dataset Structure
- `data/` contains the experiment data.
- `DataDictionary.txt` explains the meaning of columns and codes in various files.
- `DeID_Dems.csv` gives an overview of the subjects' demographic attributes.
- `MMN_ElectrodeCoords_MA2.ced` contains information about the EEG electrode positions.
- `Pre-processing_Pseudo.txt` explains the pre-processing pipeline in pseudocode applied on the EEG data.
- `scripts/` contains the MATLAB scripts that were used to execute the experiment.

### Filename Format

```
CC_EEG_s[PID]_[CONDITION].mat
```
PID is the patient ID (e.g. `101`), while CONDITION denotes the condition (`P` means "with schizophrenia", `N` means "without schizophrenia"). All patients with PID <= 147 have schizophrenia and all patients with PID >= 149 do NOT have schizophrenia and hence belong to the control group.

### Fields in each File
A `.mat` file can be read in python as follows:
```python
from scipy.io import loadmat
filename = "CC_EEG_s101_P.mat"
mat = loadmat(filename, simplify_cells=True)
```
(A field "fieldname" can be read from `mat` as `mat["fieldname"]`.)

Then `mat` contains (among others) the following fields and subfields
- `EEG`
    - `data`: EEG data of shape `(#channels, time_len)`. E.g. a recording of 30 minutes with 64 channels and a sampling rate of 1000 Hz will have shape `(64, 1800000)`.
    - `event`: Contains a list of dictionaries, each entry (each event) having the following description:
        - `latency`: The onset of the event, measured as the index in the time-dimension `time_len`. As noted in the paper, each stimulus has a response window of 850 ms.
        - `type`: The type of event. This type will be a number of up to 3 digits. In order to decode them, please refer to the `Triggers` section in `DataDictionary.txt`. Typically, a trial starts with a Stimulus Trigger, followed by a Response Trigger (or `105` if no response was given, i.e. the patient did not press a button) and then a Feedback Trigger.
    - `srate`: Sampling Rate (Hz)

Alternatively, event information for any `CC_EEG_s[PID]_[CONDITION].mat` file can also be read from the accompanying `CC_Beh_s[PID]_[CONDITION].mat` file. There, trials are organized in the 4 blocks described in the paper. Information about a trial can be read as
```python
beh_mat = loadmat("CC_Beh_s[PID]_[CONDITION].mat", simplify_cells=True)
block = 0          # can be 0, 1, 2 or 3
trial_number = 5   # number of the trial
trial_info = beh_mat["Beh"][block]["data_block"][trial_number]
```
`trial_info` can then be decoded using the `Behavioral Data` section of `DataDictionary.txt`.

## License
By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt).