timestamp
int64 1,767B
1,767B
| session_time
int64 41
1,767B
| mouse
dict | keyboard
dict | camera
dict | environment
dict | type
stringclasses 1
value | button
stringclasses 1
value | target
null | neural_context
null |
|---|---|---|---|---|---|---|---|---|---|
1,767,171,126,971
| 1,767,171,126,971
| null | null | null | null |
mouse_click
|
left
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1,767,171,127,074
| 41
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| 56
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| 72
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| 89
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| 105
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| 122
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{
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| 148
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{
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{
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{
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1,767,171,127,195
| 162
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{
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{
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{
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1,767,171,127,214
| 181
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{
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{
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{
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1,767,171,127,234
| 201
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{
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{
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{
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1,767,171,127,251
| 218
|
{
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{
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{
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| null | null | null | null |
1,767,171,127,292
| 259
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{
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{
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{
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| null | null | null | null |
1,767,171,127,310
| 277
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{
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{
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1,767,171,127,338
| 305
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{
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{
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{
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| null | null | null | null |
1,767,171,127,355
| 322
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{
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|
{
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{
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{
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| null | null | null | null |
1,767,171,127,380
| 347
|
{
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|
{
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|
{
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| null | null | null | null |
1,767,171,127,398
| 365
|
{
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|
{
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|
{
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|
{
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| null | null | null | null |
1,767,171,127,414
| 381
|
{
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|
{
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|
{
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|
{
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| null | null | null | null |
1,767,171,127,430
| 397
|
{
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|
{
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{
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| null | null | null | null |
1,767,171,127,447
| 414
|
{
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|
{
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{
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| null | null | null | null |
1,767,171,127,463
| 430
|
{
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|
{
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{
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| null | null | null | null |
1,767,171,127,479
| 446
|
{
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{
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{
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| null | null | null | null |
1,767,171,127,495
| 462
|
{
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{
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| null | null | null | null |
1,767,171,127,512
| 479
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{
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{
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| null | null | null | null |
1,767,171,127,529
| 496
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{
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| null | null | null | null |
1,767,171,127,546
| 513
|
{
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{
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| null | null | null | null |
1,767,171,127,580
| 547
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{
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1,767,171,127,596
| 563
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{
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| null | null | null | null |
1,767,171,127,614
| 581
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{
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| null | null | null | null |
1,767,171,127,630
| 597
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{
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{
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| null | null | null | null |
1,767,171,127,646
| 613
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{
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{
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{
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| null | null | null | null |
1,767,171,127,662
| 629
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{
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{
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{
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| null | null | null | null |
1,767,171,127,679
| 646
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{
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{
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| null | null | null | null |
1,767,171,127,695
| 662
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{
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{
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| null | null | null | null |
1,767,171,127,711
| 678
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{
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{
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| null | null | null | null |
1,767,171,127,732
| 699
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{
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{
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{
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{
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1,767,171,127,749
| 716
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{
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{
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| null | null | null | null |
1,767,171,127,765
| 732
|
{
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{
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|
{
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| null | null | null | null |
1,767,171,127,785
| 752
|
{
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{
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| null | null | null | null |
1,767,171,127,801
| 768
|
{
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{
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{
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| null | null | null | null |
1,767,171,127,817
| 784
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{
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{
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| null | null | null | null |
1,767,171,127,835
| 802
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{
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{
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| null | null | null | null |
1,767,171,127,851
| 818
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{
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{
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1,767,171,127,869
| 836
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{
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{
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1,767,171,127,889
| 856
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{
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{
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1,767,171,127,905
| 872
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{
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1,767,171,127,922
| 889
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1,767,171,127,938
| 905
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{
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1,767,171,127,953
| 920
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{
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1,767,171,127,970
| 937
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0,
0
]
}
|
{
"active_targets": [],
"fps": 27
}
| null | null | null | null |
End of preview. Expand
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Dataset Card for BCI-FPS MOTOR_IMAGERY Dataset
Dataset Description
UNDER DEVELOPMENT for TESTING
This dataset was made using app in the /generator/ folder which is under development testing.
Use case ideas and concepts:
Simulated data for Intent testing, does not use real Neuralink/BCI hardware signals.
BCI Intent Data Study and Testing (conceptual early design) for training machine learning models for neural signal decoding without needing large scale real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies.
-Augmenting real-world BCI data with synthetic samples to improve model robustness and diversity, as in GAN-based approaches.
-Testing and calibrating BCI systems for motor imagery tasks like prosthetic control before human trials.
-Simulating neural responses in assistive technologies for disabled individuals, enabling faster iteration in labs like Neuralink.
Dataset Summary
- Training Mode: MOTOR IMAGERY
- Session ID: bci_fps_motor_imagery_1767171179245
- Duration: 52 seconds
- Sampling Rate: 1000 Hz
- Neural Channels: 32
- Data Points: 11,314
Supported Tasks
- Motor Imagery Training for prosthetic control
- Neural Decoding: Training models to decode user intent from neural signals
- BCI Calibration: Providing ground truth data for BCI system calibration
- Disability Research: Supporting development of assistive technologies
Languages
English (interface and documentation)
Data Instances
{
"timestamp": 1767171127035,
"session_time": 2,
"channels": {
"channel_0": 0.7145493839481488,
"channel_1": 0.6894168445867142,
"channel_2": 0.08142761930267149,
"channel_3": -0.4847495027079371,
"channel_4": -0.7151022782142631,
"channel_5": -0.30725177077599913,
"channel_6": 0.41521139153211245,
"channel_7": 0.8975965762479154,
"channel_8": 0.40940126876082966,
"channel_9": -0.4091680578228324,
"channel_10": -0.8292701881852992,
"channel_11": -0.5904045145284711,
"channel_12": 0.12196528544955941,
"channel_13": 0.7040845591149026,
"channel_14": 0.5296790688037042,
"channel_15": 0.018181536760527098,
"channel_16": -0.6973668262179662,
"channel_17": -0.7437997196398959,
"channel_18": -0.10714886215673841,
"channel_19": 0.6246891444747351,
"channel_20": 0.8560240877317689,
"channel_21": 0.155749695078711,
"channel_22": -0.4754514086663171,
"channel_23": -0.7632646743624881,
"channel_24": -0.42658424045199833,
"channel_25": 0.47380668620054267,
"channel_26": 0.7558851981047924,
"channel_27": 0.5145527444334146,
"channel_28": -0.22899647502709344,
"channel_29": -0.8498710316208474,
"channel_30": -0.5816021940073672,
"channel_31": 0.2096020563849897
},
"intent_context": {
"mouse_movement": [
0,
0
],
"keyboard_state": {
"mouse": false
},
"camera_rotation": [
0,
0,
0
],
"active_targets": 0
}
}
Data Fields
See metadata.json for complete schema documentation.
Source Data
- Platform: Web-based BCI-FPS Training Environment
- Sampling Rate: 1000 Hz
- Collection Method: Real-time telemetry during BCI training tasks
- Neural Simulation: Synthetic neural data representing ideal BCI signals
Annotations
- Annotation process: Automatic intent labeling during gameplay
- Annotation types: Motor imagery, visual stimuli, handwriting intent
- Who annotated: System automatically labels based on game state
Personal and Sensitive Information
No personal information is collected. All data is synthetic/anonymous.
Social Impact
This dataset enables research in:
- Neuralink-style brain-computer interfaces
- Assistive technologies for disabled individuals
- Human-AI interaction systems
- Neural decoding algorithms
Discussion of Biases
Synthetic neural data may not perfectly represent biological signals. Results should be validated with real neural recordings.
Other Known Limitations
- Simulated neural signals
- Idealized game environment
- Limited to specific training tasks
Additional Information
Dataset Curators
huggingface.co/webXOS
Licensing Information
MIT License
Citation Information
@misc{bci_fps_motor_imagery,
title={BCI-FPS motor_imagery Dataset},
author={webXOS,
year={2024},
note={for testing and development purposes}
}
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