Datasets:
participant_id
stringclasses 18
values | sentence_id
stringlengths 4
4
| sentence_text
stringlengths 19
97
| word_eeg_segments
listlengths 4
21
| num_words
int32 4
21
| eeg_shape
stringlengths 27
29
| has_gaze_alignment
bool 1
class |
|---|---|---|---|---|---|---|
P28
|
S169
|
There is a stand in the room.
|
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]
] | 7
|
7words_x_92time_x_4channels
| true
|
P05
|
S002
|
There are a table with two cakes and champagne glasses and two women serving cake.
| [[[0.18574658036231995,-0.22647839784622192,0.2588600814342499,0.11357714235782623],[0.4169266521930(...TRUNCATED)
| 15
|
15words_x_96time_x_4channels
| true
|
P38
|
S189
|
Danny left the chair with a blue telescope.
| [[[0.2775115668773651,-0.11571120470762253,-0.3692244589328766,0.15793390572071075],[0.1107619628310(...TRUNCATED)
| 8
|
8words_x_128time_x_4channels
| true
|
P09
|
S115
|
A horse reaching over a fence is eating wild grass.
| [[[0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0],[0.0,0.0(...TRUNCATED)
| 10
|
10words_x_10time_x_4channels
| true
|
P09
|
S121
|
A man is covering his eyes next to cases of bananas.
| [[[-0.1739812046289444,-0.03538189455866814,-0.9329397678375244,0.900295078754425],[0.38966995477676(...TRUNCATED)
| 11
|
11words_x_122time_x_4channels
| true
|
P20
|
S179
|
A small child wrapped in a towel lies on a bed.
| [[[0.19318830966949463,0.1853501945734024,0.16026456654071808,0.3623031973838806],[-0.39394164085388(...TRUNCATED)
| 11
|
11words_x_76time_x_4channels
| true
|
P09
|
S120
|
An elephant is sticking its trunk through a fence.
| [[[1.9545403718948364,0.29911863803863525,1.2166221141815186,0.09011512249708176],[2.737673282623291(...TRUNCATED)
| 9
|
9words_x_128time_x_4channels
| true
|
P32
|
S181
|
This is a white second hand on the clock.
| [[[0.34347274899482727,0.3076154887676239,-0.09343929588794708,-0.2152635157108307],[0.1669568717479(...TRUNCATED)
| 9
|
9words_x_128time_x_4channels
| true
|
P42
|
S177
|
A man is putting a turkey into the oven.
| [[[0.35474714636802673,-0.1277618706226349,-0.2807841897010803,-0.3723597228527069],[0.7540289759635(...TRUNCATED)
| 9
|
9words_x_100time_x_4channels
| true
|
P20
|
S170
|
There is a man sitting on a cement block.
| [[[0.47677257657051086,0.315810889005661,0.4063529670238495,0.32287442684173584],[-1.058997511863708(...TRUNCATED)
| 9
|
9words_x_84time_x_4channels
| true
|
End of preview. Expand
in Data Studio
EMMT EEG-to-Text Dataset
This dataset contains preprocessed EEG brain signals recorded while participants read English sentences and translated them to Czech. It is derived from the EMMT (Eyetracked Multi-Modal Translation) corpus.
Dataset Structure
Splits
| Split | Samples | Description |
|---|---|---|
| Train | 576 | Training set (70% of participants) |
| Validation | 128 | Validation set (15% of participants) |
| Test | 160 | Test set (15% of participants) |
Important: Splits are done by participant to prevent data leakage. Different participants may read the same sentences, ensuring the model learns to generalize across individuals.
Data Fields
Each sample contains:
participant_id(string): Anonymized participant identifier (e.g., "P01", "P02")sentence_id(string): Sentence identifier (e.g., "S001", "S062")sentence_text(string): The English sentence that was being readword_eeg_segments(3D array): Word-level EEG data- Shape:
[num_words, time_samples, 4_channels] - Each word has its own EEG segment extracted using gaze-timestamp alignment
- Time samples vary per word (typically 50-200 samples = 200-800ms @ 256Hz)
- Shape:
num_words(int): Number of words in the sentenceeeg_shape(string): Human-readable shape info (e.g., "15words_x_128time_x_4channels")has_gaze_alignment(bool): Whether gaze-based alignment was used
Preprocessing Pipeline
The EEG data has been preprocessed with standard neuroscience techniques:
- Notch Filter: 50Hz (removes electrical power line noise)
- Bandpass Filter: 0.5-50Hz (keeps relevant brain frequencies, removes drift and high-freq noise)
- Robust Scaling: Normalization robust to eye blinks and movement artifacts
- Gaze-Timestamp Alignment: EEG segments aligned to actual word reading times using eye-tracking fixations
Dataset Statistics
Overall Statistics
- Total Samples: 864
- Unique Participants: 21
- Unique Sentences: 200
- Average Words per Sentence: 11.0
Training Set
- Samples: 576
- Participants: ~70% of total
- Purpose: Model training
Validation Set
- Samples: 128
- Participants: ~15% of total (different from training)
- Purpose: Hyperparameter tuning, model selection
Test Set
- Samples: 160
- Participants: ~15% of total (different from training & validation)
- Purpose: Final performance evaluation on unseen participants
EEG Channel Information
The 4 EEG channels capture different brain regions relevant to language processing:
| Channel | Location | Brain Region | Function |
|---|---|---|---|
| RAW_TP9 | Left temporal-parietal | Language processing | Semantic understanding, memory |
| RAW_AF7 | Left anterior-frontal | Executive function | Attention, working memory |
| RAW_AF8 | Right anterior-frontal | Executive function | Attention, cognitive control |
| RAW_TP10 | Right temporal-parietal | Visual-spatial | Visual processing, attention |
Original EMMT Experiment
The data comes from the EMMT (Eyetracked Multi-Modal Translation) corpus, which recorded participants performing a multimodal translation task:
- READ: Read English sentence aloud
- TRANSLATE: Translate to Czech aloud
- SEE: View accompanying image
- UPDATE: Update or repeat translation
This dataset contains EEG data from the READ stage, aligned to word-level using eye-tracking fixations.
- Downloads last month
- 45
Task
: EEG-to-Text Generation (Brain-Computer Interface)
Modality
: EEG (Electroencephalography) + Eye-tracking
Channels
: 4 EEG channels (RAW_TP9, RAW_AF7, RAW_AF8, RAW_TP10)
Sampling Rate
: 256 Hz
Language
: English sentences (to be translated to Czech)
Participants
: 21 participants
Total Sentences
: 200 unique sentences
Size of downloaded dataset files:
19.9 MB
Size of the auto-converted Parquet files:
19.9 MB
Number of rows:
864