Datasets:

sajjad5221
/

eeg2text-emmt-dataset

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.	[ [ [ 0.8530703186988831, 0.5957651734352112, -1.8703969717025757, 1.0854332447052002 ], [ 0.4786102771759033, 0.9101021885871887, -2.2923431396484375, 0.8543886542320251 ], [ 0.2722378373146057, 0.34235680103302, -2.316891193389892...	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

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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 read
word_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)
num_words (int): Number of words in the sentence
eeg_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