Update README.md

README.md

@@ -1,43 +1,92 @@
----
-license: apache-2.0
-dataset_info:
-  config_name: data
-  features:
-  - name: event
-    dtype: int64
-  - name: word
-    dtype: string
-  - name: topic
-    dtype: string
-  - name: selected_topic
-    dtype: string
-  - name: semantic_relevance
-    dtype: int64
-  - name: interestingness
-    dtype: int64
-  - name: pre-knowledge
-    dtype: int64
-  - name: sentence_number
-    dtype: int64
-  - name: participant
-    dtype: string
-  - name: eeg
-    dtype:
-      array2_d:
-        shape:
-        - 32
-        - 2001
-        dtype: float64
-  splits:
-  - name: train
-    num_bytes: 11925180913
-    num_examples: 23270
-  download_size: 11927979870
-  dataset_size: 11925180913
-configs:
-- config_name: data
-  data_files:
-  - split: train
-    path: data/train-*
-  default: true
----
+---
+license: apache-2.0
+dataset_info:
+  config_name: data
+  features:
+  - name: event
+    dtype: int64
+  - name: word
+    dtype: string
+  - name: topic
+    dtype: string
+  - name: selected_topic
+    dtype: string
+  - name: semantic_relevance
+    dtype: int64
+  - name: interestingness
+    dtype: int64
+  - name: pre-knowledge
+    dtype: int64
+  - name: sentence_number
+    dtype: int64
+  - name: participant
+    dtype: string
+  - name: eeg
+    dtype:
+      array2_d:
+        shape:
+        - 32
+        - 2001
+        dtype: float64
+  splits:
+  - name: train
+    num_bytes: 11925180913
+    num_examples: 23270
+  download_size: 11927979870
+  dataset_size: 11925180913
+configs:
+- config_name: data
+  data_files:
+  - split: train
+    path: data/train-*
+  default: true
+---
+
+We release a novel dataset containing 23,270 time-locked (0.7s) word-level EEG recordings acquired from participants who read both text that was semantically relevant and irrelevant to self-selected topics.
+
+Submitted to ICLR 2025. The raw EEG data and the datasheet will be avaialble after acceptance to avoid disclosure of the authors' identity.
+
+See [code repository][1] for benchmark results.
+
+
+
+
+Explanations of the variables:
+
+- **event** corresponds to a specific point in time during EEG data collection and represents the onset of an event (presentation of a word)
+- **word** is a word read by the participant
+- **topic** is the topic of the document to which the **word** belongs to
+- **selected topic** indicates the topic the participant has selected
+- **semantic relevance** indicates whether the word is semantically relevant (expressed as 1) or semantically irrelevant (expressed as 0) to the topic selected by the participant
+- **interestingness** indicates the participant's interest in the topic of a document
+- **pre-knowledge** indicates the participant's previous knowledge about the topic of the document
+- **sentence number** represents the sentence number to which the word belongs
+- **eeg** - brain recordings having a shape of 32 x 2001 for each word 
+
+The dataset can be downloaded and used as follows:
+
+```py
+import numpy as np
+from datasets import load_dataset
+
+# Load the cleaned version of the dataset
+d = load_dataset("Quoron/EEG-semantic-text-relevance", "data")
+
+# See the structure of the dataset
+print(d)
+
+# Get the first entry in the dataset
+first_entry = d['train'][0]
+
+# Get EEG data as numpy array in the first entry
+eeg = np.array(first_entry['eeg'])
+
+# Get a word in the first entry
+word = first_entry['word']
+
+```
+
+We recommend using the Croissant metadata to explore the dataset.
+
+
+  [1]: https://anonymous.4open.science/r/EEG-semantic-text-relevance-651D