Aditharavind/laya-neuro-decisions

laya-neuro Decisions

Part of laya-neuro, an unofficial, independent fine-tune of Laya by Nandhakishor M — not affiliated with or endorsed by the original project. This dataset is a derivative of STEW (Lim, Sourina & Wang), via its Hugging Face mirror monster-monash/STEW (CC BY 4.0) — engineered features and Laya-format rows derived from that source's raw EEG, not original data collection.

EEG-derived cognitive-workload decision states for Laya's typed-decision fine-tuning recipe. Each row is one 2-second EEG epoch: a state (10 engineered features — relative band power per canonical band plus literature-grounded ratios), a fixed questions schema (one noul/binary question, high_workload), and gold — a confident soft probability distribution matching STEW's binarized self-reported workload label.

How it was generated

STEW's HF mirror provides 28,512 pre-segmented, 14-channel (Emotiv EPOC), 128 Hz, 2-second epochs with a binarized high/low workload label and a subject ID. eeg/features.py (in the source repo) reduces each epoch to ~10 named features via Welch's method PSD: relative power in 5 canonical bands (delta/theta/alpha/beta/gamma), frontal theta, posterior alpha, their ratio, the engagement index (beta/(alpha+theta)), and theta/alpha ratio. eeg/build_dataset.py turns those into Laya-format rows and produces two split protocols.

Splits — two protocols, always reported separately

split	protocol	rows	notes
cross_subject/{train,val,test}	subject-disjoint	17,820 / 3,564 / 7,128	the honest protocol — test is STEW's own pre-verified fold of 12 subjects with zero epochs anywhere in train/val
within_subject/{train,val,test}	naive random	19,960 / 4,276 / 4,276	ignores subject boundaries — kept only for comparison with literature numbers that use this easier, leakage-prone protocol

stew_features.parquet also included: the full 28,512-row engineered feature table (with label and subject_id columns) used to train the classical ML baselines (SVM/RF/LDA) reported alongside Laya.

Schema

{
  "state": "{\"delta_mean\": 0.618, \"theta_mean\": 0.141, \"alpha_mean\": 0.083, \"beta_mean\": 0.113, \"gamma_mean\": 0.048, \"frontal_theta\": 0.145, \"posterior_alpha\": 0.109, \"frontal_theta_posterior_alpha_ratio\": 1.326, \"engagement_index\": 0.504, \"theta_alpha_ratio\": 1.706}",
  "questions": "{\"high_workload\": {\"type\": \"noul\", \"instructions\": \"Given this subject's EEG-derived features for this 2-second window, is this subject currently experiencing high cognitive workload?\"}}",
  "gold": "{\"high_workload\": {\"probabilities\": {\"true\": 0.95, \"false\": 0.05}}}"
}

Use

from datasets import load_dataset
ds = load_dataset("Aditharavind/laya-neuro-decisions", data_files="cross_subject/train.jsonl")

Or point training/preprocess.py in the source repo at these files directly.

License

CC BY 4.0, matching the source STEW mirror's license. Attribution: Lim, W.L., Sourina, O., Wang, L.P. (2018), "STEW: Simultaneous Task EEG Workload Dataset," IEEE Transactions on Neural Systems and Rehabilitation Engineering.