stat214-lab3-ridge-models

stat214-lab3-ridge-models

Per-voxel ridge regression weight matrices for the Stat 214 (Spring 2026) final project at UC Berkeley. These models predict whole-brain BOLD signal from spoken-story transcripts on the Huth Lab fMRI dataset (Subjects 2 and 3).

Each .pkl file is a Python dict with:

D = feature dim after 4-lag concatenation, V = number of voxels per subject (94,251 for Subject 2, 95,556 for Subject 3).

Files

Loading

import pickle
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="RheaTinghe/stat214-lab3-ridge-models",
    filename="ridge_bert_var_layer8_full_train_subject2.pkl",
)
with open(path, "rb") as f:
    model = pickle.load(f)

# Predict for new z-scored test features X (T x D):
import numpy as np
X_test_z = (X_test - model["train_mean_X"]) / model["train_std_X"]
Y_pred_z = X_test_z @ model["weights"]
Y_pred = Y_pred_z * model["train_std_Y"] + model["train_mean_Y"]

Citation

@misc{stat214lab3,
  author = {Galloro, Drew and Wang, Ruihang and Khothsombath, Benjamin and Zhang, Rhea},
  title  = {Stat 214 Lab 3: voxel-wise encoding of spoken stories},
  year   = {2026},
  note   = {UC Berkeley Spring 2026},
}