Error-Related Potential in static and motion conditions during gait control with an exoskeleton
Year: 2025
Type: dataset
Abstract: Description This dataset contains EEG signals from experiments designed to evoke Error-Related Potentials (ErrP) under static and motion conditions, during gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP in the static condition is elicited using tactile feedback that activates when the participant attempts to initiate gait through motor imagery and, once they deactivate, the exoskeleton starts walking. In the motion condition, the same tactile stimulus is triggered while walking and, once they deactivate, the exoskeleton stops. The participants were provided with information about the experimental procedure and signed a written informed consent form, as well as consent for video and image recording. The study was carried out in accordance with the Declaration of Helsinki of the World Medical Association and was approved by the Office of Responsible Research at the Miguel Hernández University of Elche (DIS.JAP.09.21) for healthy participants and by the ethics committees of the University Hospital Complex of Toledo (Ref. No. 986; 29/03/2023) and Institut Guttmann (CEI 23/16) for incomplete SCI patients. The experiment consists in a circuit with color marks on the floor, with two types of regions: Gait Regions, where the subject has to keep walking, and Stop Regions, where the subject tries to stop the exoskeleton using motor imagination. At the beginning of each repetition, participants remain stationary and engage in two mental tasks: Relax (R) and Motor Imagery (I) of walking to activate the exoskeleton. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For example, during RC (Relax Correct), the subject maintains an idle state, whereas during RE (Relax Error), the exoskeleton activates unexpectedly. Conversely, in IC, the subject imagines the sensation of starting to walk in their muscles, and the exoskeleton activates, but during IE, the exoskeleton does not move despite the motor imagery. When the stimulus activates before starting to walk, it remains active for 2 seconds to alert the subject about the impending movement. Therefore, ErrP in static is elicited by the feedback in RE and can be compared with the absence of ErrP in IC, where the stimulus activates but should not evoke an error. Once the participant is walking, they must perform two mental tasks: Relax (R), at Gait Regions to keep walking, and Motor Imagery (I) of stop, at Stop Regions to deactivate the exoskeleton and stop walking. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For instance, the subject enters in the Gait Region and maintains an idle state while walking to continue with the gait. In RC (Relax Correct) they cross the region without any stop, but in RE (Relax Error) the feedback activates and the exoskeleton erroneously stops. Conversely, in IC (Imagination Correct), the subject imagines the sensation of stop walking in their muscles, the feeedback activates and the exoskeleton stops the gait, but during IE (Imagination Error), they walk through the region and the exoskeleton does not stop despite the motor imagery. Therefore, ErrP in motion is elicited by the stimuli in RE and can be compared with the absence of ErrP (NoErrP) in IC, where the stimulus activates but should not evoke an error. Since it is an open-loop experiment and the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). In addition, the system is an states-machine, so when the exoskeleton stops, participants must perform the same static mental tasks to start the gait again and complete the circuit. In this particular case, they perform Motor Imagery tasks in every region except in the last Gait Region, where they must perform a Relax task to remain stationary and end the trial. These experiments were performed by able‑bodied participants and patients with spinal cord injury (SCI) employing two different robots: exoskeleton H3 and Lokomat. There is a variant of the experiments, that was performed only by SCI patients, which implies the use of tSCS that activates at the same time as the exoskeleton starts walking. It is important to consider that tSCS introduces artifacts in the EEG signals at 30 Hz and its harmonics. The original predefined number of sessions was 5, but some participants (R21, R24, R34, R41) could only participate in 4, due to external inconveniences. The number of trials is variable in participants, based on their fatigue and their ability to walk and mantain stability with the exoskeleton. The characteristics of each subject are summarized below: Subject Condition Exoskeleton tSCS R01 Healthy H3 No R21 Healthy H3 No R24 SCI H3 No R25 SCI H3 No R33 SCI Lokomat No R34 SCI Lokomat No R39 SCI H3 Yes R40 SCI H3 Yes R41 SCI H3 Yes R42 SCI Lokomat Yes R43 SCI Lokomat Yes Data information A trial consists of a Matlab structure that stores all information related to the trial experiment. data_EEG: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes). data_preprocessed_EEG: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in session.conf.info.preprocessingSteps.ListPreprocessingSteps. trigger_EEG: Information related to signal quality and missing data while recording. data_EXO: Exoskeleton recorded data with a sampling rate of 250Hz. data_preprocessed_EXO: The same data recorded by the exoskeleton in data_EXO, since it does not require the application of any filter. trigger_EXO: Empty vector. data_Actuators: Arduino response when activates (1) and deactivates (-1) the feedback and when activates (20) and deactivates (-20) tSCS. data_preprocessed_Actuators: The same Arduino resposes recorded in data_Actuators, because it does not require any filter application. trigger_Actuators: Empty vector. task_EEG: Vector that associates a task to each signal sample. task_index_EEG: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. task_order_EEG: Vector that increments a unit with each task change. event_EEG: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino, to activate (20) and deactivate (-20) the tSCS. conf: Configuration employed for data acquisition and preprocessing. acquisition: User and signals acquisition information. user_code: User code name. feedback: Trial in openloop (User do not have control of the system). feedbackErrP: Feedback type employed during the trial. readfile: Path to read files after its acquisition. saveSession_Script: Script used to save the recorded data. writeResults: Path to save the recorded data. device: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. task: Information about tasks occurring during the trial. task_list: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task). sequence_tasks: List of tasks in order of execution. sequence_times: List with the duration of each task in the sequence. deviceOutput: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off. eye_index: Indexes of EOG electrodes. EEG_index: Indexes of EEG electrodes. inertial_index: Indexes of inertial electrodes. file_name: Trial name. num_epochs: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. preadjustment: Empty list. preprocessing: Information of the preprocessing filters, parameters and order of application. processing: Not necessary for this analysis. static: Information used internally by the architecture for its correct operation. info: Important information about filters, their order and indexes in data_processed_EEG. times: Struct with information of the devices synchronization and preprocessing times. times_processing: Processing duration times.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7220920928