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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'session_info', 'dataset_info', 'research_applications', 'data_schema', 'huggingface'}) and 10 missing columns ({'neural_context', 'environment', 'mouse', 'session_time', 'keyboard', 'camera', 'button', 'target', 'timestamp', 'type'}).
This happened while the json dataset builder was generating data using
hf://datasets/webxos/BCI-FPS/metadata.json (at revision a525e5235955a1c45616f5059a9779483fa6d20d)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
dataset_info: struct<name: string, description: string, version: string, license: string, citation: string>
child 0, name: string
child 1, description: string
child 2, version: string
child 3, license: string
child 4, citation: string
session_info: struct<session_id: string, mode: string, start_time: string, duration_ms: int64, sampling_rate_hz: i (... 29 chars omitted)
child 0, session_id: string
child 1, mode: string
child 2, start_time: string
child 3, duration_ms: int64
child 4, sampling_rate_hz: int64
child 5, neural_channels: int64
data_schema: struct<neural_data: struct<timestamp: string, session_time: string, channels: string, intent_context (... 184 chars omitted)
child 0, neural_data: struct<timestamp: string, session_time: string, channels: string, intent_context: string>
child 0, timestamp: string
child 1, session_time: string
child 2, channels: string
child 3, intent_context: string
child 1, intent_stream: struct<timestamp: string, mouse: string, keyboard: string, camera: string, environment: string>
child 0, timestamp: string
child 1, mouse: string
child 2, keyboard: string
child 3, camera: string
child 4, environment: string
child 2, handwriting_samples: struct<letter: string, samples: string>
child 0, letter: string
child 1, samples: string
research_applications: list<item: string>
child 0, item: string
huggingface: struct<compatible: bool, task_categories: list<item: string>, task_ids: list<item: string>, language (... 58 chars omitted)
child 0, compatible: bool
child 1, task_categories: list<item: string>
child 0, item: string
child 2, task_ids: list<item: string>
child 0, item: string
child 3, language: list<item: string>
child 0, item: string
child 4, size_categories: list<item: string>
child 0, item: string
to
{'timestamp': Value('int64'), 'session_time': Value('int64'), 'mouse': {'position': List(Value('float64')), 'delta': List(Value('int64')), 'buttons': Value('int64')}, 'keyboard': {'mouse': Value('bool')}, 'camera': {'position': List(Value('float64')), 'rotation': List(Value('float64'))}, 'environment': {'active_targets': List({'position': List(Value('float64')), 'distance': Value('float64')}), 'fps': Value('int64')}, 'type': Value('string'), 'button': Value('string'), 'target': Value('null'), 'neural_context': {'channel_0': Value('float64'), 'channel_1': Value('float64'), 'channel_2': Value('float64'), 'channel_3': Value('float64'), 'channel_4': Value('float64'), 'channel_5': Value('float64'), 'channel_6': Value('float64'), 'channel_7': Value('float64'), 'channel_8': Value('float64'), 'channel_9': Value('float64'), 'channel_10': Value('float64'), 'channel_11': Value('float64'), 'channel_12': Value('float64'), 'channel_13': Value('float64'), 'channel_14': Value('float64'), 'channel_15': Value('float64'), 'channel_16': Value('float64'), 'channel_17': Value('float64'), 'channel_18': Value('float64'), 'channel_19': Value('float64'), 'channel_20': Value('float64'), 'channel_21': Value('float64'), 'channel_22': Value('float64'), 'channel_23': Value('float64'), 'channel_24': Value('float64'), 'channel_25': Value('float64'), 'channel_26': Value('float64'), 'channel_27': Value('float64'), 'channel_28': Value('float64'), 'channel_29': Value('float64'), 'channel_30': Value('float64'), 'channel_31': Value('float64')}}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'session_info', 'dataset_info', 'research_applications', 'data_schema', 'huggingface'}) and 10 missing columns ({'neural_context', 'environment', 'mouse', 'session_time', 'keyboard', 'camera', 'button', 'target', 'timestamp', 'type'}).
This happened while the json dataset builder was generating data using
hf://datasets/webxos/BCI-FPS/metadata.json (at revision a525e5235955a1c45616f5059a9779483fa6d20d)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
timestamp
int64 | session_time
int64 | mouse
dict | keyboard
dict | camera
dict | environment
dict | type
string | button
string | target
null | neural_context
null |
|---|---|---|---|---|---|---|---|---|---|
1,767,171,126,971
| 1,767,171,126,971
| null | null | null | null |
mouse_click
|
left
| null | null |
1,767,171,127,074
| 41
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,089
| 56
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,105
| 72
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,122
| 89
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,138
| 105
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,155
| 122
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,181
| 148
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,195
| 162
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,214
| 181
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,234
| 201
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,251
| 218
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,292
| 259
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,310
| 277
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,338
| 305
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,355
| 322
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,380
| 347
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,398
| 365
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,414
| 381
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,430
| 397
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,447
| 414
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,463
| 430
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,479
| 446
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,495
| 462
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,512
| 479
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,529
| 496
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 35
}
| null | null | null | null |
1,767,171,127,546
| 513
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,580
| 547
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,596
| 563
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,614
| 581
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,630
| 597
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,646
| 613
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,662
| 629
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,679
| 646
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,695
| 662
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,711
| 678
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,732
| 699
|
{
"position": [
0,
0
],
"delta": [
0,
0
],
"buttons": 0
}
|
{
"mouse": false
}
|
{
"position": [
0,
1.6,
0
],
"rotation": [
0,
0,
0
]
}
|
{
"active_targets": [],
"fps": 26
}
| null | null | null | null |
1,767,171,127,749
| 716
|
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YAML Metadata
Warning:
The task_categories "brain-computer-interface" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata
Warning:
The task_ids "motor-imagery" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata
Warning:
The task_ids "intent-decoding" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata
Warning:
The task_ids "visual-evoked-potentials" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
YAML Metadata
Warning:
The task_ids "handwriting-recognition" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Dataset Card for BCI-FPS MOTOR_IMAGERY Dataset
Dataset Description
UNDER DEVELOPMENT
Use Cases: BCI Intent Data Study and Testing (conceptual early design)
Training machine learning models for neural signal decoding without needing large real neural datasets, addressing data scarcity and privacy issues.
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.
Developing predictive models for intent recognition in human-AI interactions and rehabilitative BCIs.
Enhancing clinical research datasets for disease risk assessment and patient outcome prediction in neuroengineering.
Validating algorithms in frontier labs (e.g., Neuralink, Paradromics) for high-data-rate implants by generating idealized signals.
This dataset contains high-bandwidth neural training data collected from BCI-FPS, a specialized training platform for brain-computer interface research.
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)
Dataset Structure
Data Instances
{
"timestamp": 1767171127035,
"session_time": 2,
"channels": {
"channel_0": 0.7145493839481488,
"channel_1": 0.6894168445867142,
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"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,
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}
Data Fields
See metadata.json for complete schema documentation.
Dataset Creation
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.
Considerations for Using the Data
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
BCI-FPS Research Team
Licensing Information
MIT License
Citation Information
@misc{bci_fps_motor_imagery_2024,
title={BCI-FPS motor_imagery Training Dataset},
author={Neuralink Research},
year={2024},
note={High-frequency intent decoding data for brain-computer interface development}
}
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