DOI: 10.48328/tudatalib-1963
title: Human 64-Channel EEG Data (Fully Processed) in Passive Knee Exoskeleton Interaction

description: **Overview** This dataset contains electroencephalography (EEG) recordings collected during an experiment investigating human–machine interaction with a passive knee exoskeleton. The study focused on identifying cortical features that reflect user preferences and perceived difficulty when performing a motor tracking task under different physical effort conditions. **Participants** - **Number of participants:** 14 - **Demographics:** 19-35 years old, mean age 23 years old, 12 Females, 2 Males - **Inclusion criteria:** Healthy adults with no known neurological or musculoskeletal disorders - **Ethics:** All participants provided written informed consent before participation. The study was approved by the ethics committee of TU Darmstadt (Protocol Number: EK 07/2023). **Experimental Design** - Participants were seated on a chair and wore a passive knee exoskeleton on their right leg. - The exoskeleton was equipped with a pneumatic artificial muscle (PAM) mounted above the knee. - Subjects were instructed to swing their right leg to track an individualized sinusoidal reference trajectory displayed on a screen. The vertical position of a marker on the screen was controlled by flexion and extension of the right knee. - Three different pneumatic pressure levels were applied to the PAM, creating different physical effort conditions. - After each condition, participants rated the perceived difficulty of the task on a 1–10 scale (higher values = more difficult). **Data Acquisition** - **EEG system:** 64-channel EEG system (LiveAmp Brain Product, Germany) - **Electrode placement:** Standard 10–20 layout - **Sampling rate:** 500 Hz - **Recorded signals:** - Continuous EEG across all conditions - Event markers (trial start, trial end, pressure level, condition type, Start/End of Flexion/Extension moments) - Subjective difficulty ratings in Trials_Info.mat files **Data Format and Structure** - **Format:** EEGLAB `.set`/`.fdt` - **Organization:** - Participants_Cleaned_with_ICA_Epoched_data folder contains fully processed EEG data (channel level and component level data in epoched format with time-warping info) - Participants_Cleaned_with_ICA_nonEpoched_data folder contains fully processed EEG data (channel level and component level data in continuous format) - Trials_Info folder contains events latencies, condition labels, and subjective ratings - STUDY folder contains the .study files which were generated from repeated K-mean clustering (2000 repetitions) - Events folder contains the text files including the type, description, and latencies of experimental events and synthesized events: - Experimental Events (timely ordered): - TS_Trial_Start: Start of Trial - SB_Start_Beep: First single-beep auditory signal meaning the user should not perform actions which deteriorates EEG signals - PC_Pressure_Change: The moment that compressor changes the PAM pressure (has specific sound effect whether pressure is increasing or decreasing) - SM_Start_Move: Second single-beep auditory signal with higher pitch (to differentiate from the first one) meaning user should start the tracking task - FB_Finish_Beep: A double-beep auditory signal with the same pitch as the first single-beep signal meaning the user should stop the tracking task - SP_Score_Press: The moment the experimenter enters the announced score by the user. It was tried to press the score buttons as fast as possible after the user rates the task - TE_Trial_End: End of Trial - Synthesized Events: - FlxS: Start of Flexion - FlxE: End of Flexion - ExtS: Start of Extension - ExtE: End of Extension - Metadata includes demographics **Potential Applications** - Identifying cortical features related to user effort and preference in exoskeleton-assisted movement - Studying brain dynamics during motor tracking tasks with varying physical demands - Developing passive brain–computer interfaces (BCIs) for personalized exoskeleton interaction - Benchmarking EEG preprocessing and classification methods