EEG-based brain-machine interface to a powered exoskeleton for walking and standing: A longitudinal dataset for healthy able-bodied subjects

A multimodal longitudinal dataset from seven healthy able-bodied adults containing simultaneously recorded 64 channel scalp electroencephalography (EEG), electrooculography (EOG), electrode impedances, head motion, and the internal states of a powered robotic exoskeleton (REX, by RexBionics, New Zealand) controlled by a Brain-Machine Interface (BMI) system is presented in this repository. In an initial training phase (decoder calibration), the participants performed kinesthetic motor imagery of walking and stopping motions according to audible beep instructions in a straight path with the exoskeleton controlled by an operator remotely. In a second performance phase, after decoder calibration using Localized Fisher Discriminant Analysis dimensionality reduction and a Gaussian Mixture Model classifier on lower delta band (0.1-2 Hz) EEG signals, the subjects performed closed-loop BMI training sessions in which the BMI output controlled the start and stop of walking motions of the exoskeleton. The BMI performed asynchronous classification (Walk vs. Stop states) which continuously classifies the user’s intention (Walk, Stop) to control the REX exoskeleton. While both the decoder calibration and closed-loop BMI training phases occurred every session, the decoder parameters were fixed after the fifth session, and the participants’ ability to accurately control the exoskeleton’s walking or stopping state was measured. Overall, the decoder training and BMI training sessions were executed over multiple sessions ranging from 15 to 81 days. **Data Descriptor will be submitted elsewhere for peer review.