DOI: 10.5522/04/28156016
title: Longitudinal ALS EEG Dataset for Motor Imagery Studies

description: This dataset comprises EEG recordings from eight ALS patients aged between 45.5 and 74 years. Patients exhibited revised ALS Functional Rating Scale (ALSFRS-R) scores ranging from 0 to 46, with time since symptom onset (TSSO) varying between 12 and 113 months. Notably, no disease progression was reported during the study period, ensuring stability in clinical conditions. The participants were recruited from the Penn State Hershey Medical Center ALS Clinic and had confirmed ALS diagnoses without significant dementia. This rigorous selection criterion ensured the validity and reliability of the dataset for motor imagery analysis in an ALS population.The EEG data were collected using 19 electrodes placed according to the international 10-20 system (FP1, FP2, F7, F3, FZ, F4, F8, T7, C3, CZ, C4, T8, P7, P3, PZ, P4, P8, O1, O2), with signals referenced to linked earlobes and a ground electrode at FPz. Additionally, three electrooculogram (EOG) electrodes were employed to facilitate artifact removal, maintaining impedance levels below 10 kΩ throughout data acquisition. The data were amplified using two g.USBamp systems (g.tec GmbH) and recorded via the BCI2000 software suite, with supplementary preprocessing in MATLAB. All experimental procedures adhered strictly to Penn State University’s IRB protocol PRAMSO40647EP, ensuring ethical compliance.Each participant underwent four brain-computer interface (BCI) sessions conducted over a period of 1 to 2 months. Each session consisted of four runs, with 10 trials per class (left hand, right hand, and rest) for a total of 40 trials per session. The sessions began with a calibration run to initialize the system, followed by feedback runs during which participants controlled a cursor's movement through motor imagery, specifically imagined grasping movements. The study design, focused on motor imagery (MI), generated a total of 160 trials per participant over two months.This dataset holds significance in studying the longitudinal dynamics of motor imagery decoding in ALS patients. To ensure reproducibility of our findings and to promote advancements in the field, we have received explicit permission from Prof. Geronimo of Penn State University to distribute this dataset in the processed format for research purposes. The original publication of this collection can be found below.<b>How to use this dataset: </b>This dataset is structured in MATLAB as a collection of subject-specific structs, where each subject is represented as a single struct. Each struct contains three fields:<code><strong>L</strong></code>: Trials corresponding to <i>Left</i> Motor Imagery.<code><strong>R</strong></code>: Trials corresponding to <i>Right</i> Motor Imagery.<code><strong>Re</strong></code>: Trials corresponding to <i>Rest</i> state.Each field contains an array of trials, where each trial is represented as a matrix with, <b>Rows </b>as Timestamps, and <b>Columns </b>as channels.<br><br>Primary Collection: Geronimo A, Simmons Z, Schiff SJ. Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis. Journal of neural engineering 2016 13. 10.1088/1741-2560/13/2/026002.<br><br>All code for any publications with this data has been made publicly available at the following link:<br>https://github.com/rishannp/Auto-Adaptive-FBCSP<br>https://github.com/rishannp/Motor-Imagery---Graph-Attention-Network<br>