moabb.datasets.BNCI2025_002#

class moabb.datasets.BNCI2025_002(subjects=None, sessions=None)[source]#

BNCI 2025-002 Continuous 2D Trajectory Decoding dataset.

Dataset summary

#Subj

20

#Chan

64

#Classes

3

#Trials / class

varies

Trials length

8 s

Freq

200 Hz

#Sessions

3

#Runs

1

Total_trials

varies

Participants

  • Population: Healthy (able-bodied participants) + 1 SCI participant

  • Age: 24 years

  • Handedness: {‘right’: 10}

  • BCI experience: naive BCI users in terms of motor decoding; 4 had previous EEG experience

Equipment

  • Amplifier: actiCAP, Brain Products GmbH

  • Electrodes: EEG

  • Montage: af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2

  • Reference: right mastoid

Preprocessing

  • Data state: preprocessed

  • Bandpass filter: 0.18-3 Hz

  • Steps: anti-aliasing filter (25 Hz), notch filter (50 Hz), downsampling to 100 Hz, bad channel interpolation, eye artifact subtraction (SGEYESUB algorithm), removal of frontal (AF) row channels, high-pass filter (0.18 Hz), common average re-reference, pops and drifts attenuation (HEAR algorithm), low-pass filter (3 Hz), downsampling to 20 Hz

  • Re-reference: common average reference

Data Access

Experimental Protocol

  • Paradigm: motor imagery

  • Task type: continuous 2D trajectory decoding

  • Feedback: visual (green dot showing EEG-decoded trajectory position)

  • Stimulus: visual targets (white snake/shapes on black screen)

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Dataset from [1].

Dataset Description

This dataset contains EEG recordings from participants performing a continuous 2D trajectory decoding task using attempted movement. The study investigates continuous decoding of hand movement trajectories from EEG signals, with participants tracking a moving target on screen while their dominant arm is strapped to restrict actual motor output (simulating attempted movement conditions similar to paralyzed individuals).

The experimental paradigm includes both calibration and online decoding phases, with varying levels of EEG feedback (0%, 50%, 100%) to evaluate the impact of feedback on decoding performance.

Note: Only 2 of the original 20 participants’ data is currently available on the BNCI server.

Participants

  • 10 able-bodied subjects (5 male, 5 female)

  • Mean age 24 +/- 5 years, all right-handed

  • 4 had prior EEG experience

  • Location: Institute of Neural Engineering, Graz University of Technology, Austria

Recording Details

  • Equipment: 64-channel actiCAP system (Brain Products GmbH)

  • Channels: 60 EEG + 4 EOG electrodes

  • Original sampling rate: 200 Hz

  • Electrode positions: 10-10 system with modifications (Fp1, Fp2, FT9, FT10 used as EOG; TP9, TP10 relocated to PPO1h, PPO2h)

  • Reference: Common average

  • Data synchronized using Lab Streaming Layer (LSL)

Experimental Procedure

Each session consists of:

  • Calibration phase: 2 eye runs (38 trials, 8s each) + 4 snake runs (48 trials, 23s each)

  • Online phase with 3 perception conditions: - perc0: No EEG feedback (baseline) - perc50: 50% EEG feedback - perc100: 100% EEG feedback

Trial types:

  • Snake runs: Tracking a moving white target with decorrelated x/y coordinates

  • Free runs: Tracing static shapes (diagonal/circle) at self-paced speed

Data Organization

  • 3 sessions per subject (recorded over 5 days)

  • 3 perception levels per session (perc0, perc50, perc100)

  • Files named: {subject_id}_ses{session}_perc{level}.mat

References

[1]

Kobler, R. J., Almeida, I., Sburlea, A. I., & Muller-Putz, G. R. (2022). Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant. Journal of Neural Engineering, 19(3), 036005. https://doi.org/10.1088/1741-2552/ac689f

Notes

Added in version 1.3.0.

This dataset is designed for continuous decoding research, specifically for predicting 2D hand movement trajectories from EEG. Unlike classification-based motor imagery datasets, this dataset contains continuous trajectory labels suitable for regression-based decoders.

The paradigm “imagery” is used for compatibility with MOABB’s motor imagery processing pipelines, though the actual task involves attempted (rather than imagined) movements.

See also

BNCI2014_001

4-class motor imagery dataset

BNCI2014_004

2-class motor imagery dataset

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